# CNN-paper2 **Repository Path**: wan_xin_jun/CNN-paper2 ## Basic Information - **Project Name**: CNN-paper2 - **Description**: :art: :art: 深度学习 卷积神经网络教程 ：图像识别，目标检测，语义分割，实例分割，人脸识别，神经风格转换，GAN等:art::art: https://dataxujing.github.io/CNN-paper2/ - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 2 - **Forks**: 0 - **Created**: 2020-06-16 - **Last Updated**: 2021-08-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README logo # Welcome to CNN learning **徐静** HomePage: [https://dataxujing.github.io/](https://dataxujing.github.io/) 关于CNN的基础知识及相关理论推导可以参考： ### 目录 + ResNet + Google Inception + DensenNet + SENet and ResNeXt + R-CNN, Selective Search, SPP-net + Fast R-CNN + Faster R-CNN + Light-Head R-CNN + Cascade R-CNN + SSD系列 + Mask R-CNN + YOLO + Pelee + R-FCN + FPN + RetinaNet + MegDet + DetNet + ZSD + RFBNet + DeNet + 从MobileNet到ShuffleNet + 神经风格转换 + 人脸识别 + 图像分割 + N种卷积 + GANs + anchor free ### 常用图像分类CNN结构 + ConvNet：卷积神经网络名称 + ImageNet top1 acc：该网络在ImageNet上Top1 最佳准确率 + ImageNet top5 acc：该网络在ImageNet上Top5 最佳准确率 + Published In：发表源（期刊/会议/arXiv） | ConvNet | ImageNet top1 acc | ImageNet top5 acc | Published In | |:--------------------------:|:-----------------:|:-----------------:|:------------------:| | Vgg | 76.3 | 93.2 | ICLR2015 | | GoogleNet | - | 93.33 | CVPR2015 | | PReLU-nets | - | 95.06 | ICCV2015 | | ResNet | - | 96.43 | CVPR2015 | | PreActResNet | 79.9 | 95.2 | CVPR2016 | | Inceptionv3 | 82.8 | 96.42 | CVPR2016 | | Inceptionv4 | 82.3 | 96.2 | AAAI2016 | | Inception-ResNet-v2 | 82.4 | 96.3 | AAAI2016 | |Inceptionv4 + Inception-ResNet-v2| 83.5 | 96.92 | AAAI2016 | | RiR | - | - | ICLR Workshop2016 | | Stochastic Depth ResNet | 78.02 | - | ECCV2016 | | WRN | 78.1 | 94.21 | BMVC2016 | | SqueezeNet | 60.4 | 82.5 | arXiv2017([rejected by ICLR2017](https://openreview.net/forum?id=S1xh5sYgx)) | | GeNet | 72.13 | 90.26 | ICCV2017 | | MetaQNN | - | - | ICLR2017 | | PyramidNet | 80.8 | 95.3 | CVPR2017 | | DenseNet | 79.2 | 94.71 | ECCV2017 | | FractalNet | 75.8 | 92.61 | ICLR2017 | | ResNext | - | 96.97 | CVPR2017 | | IGCV1 | 73.05 | 91.08 | ICCV2017 | | Residual Attention Network | 80.5 | 95.2 | CVPR2017 | | Xception | 79 | 94.5 | CVPR2017 | | MobileNet | 70.6 | - | arXiv2017 | | PolyNet | 82.64 | 96.55 | CVPR2017 | | DPN | 79 | 94.5 | NIPS2017 | | Block-QNN | 77.4 | 93.54 | CVPR2018 | | CRU-Net | 79.7 | 94.7 | IJCAI2018 | | ShuffleNet | 75.3 | - | CVPR2018 | | CondenseNet | 73.8 | 91.7 | CVPR2018 | | NasNet | 82.7 | 96.2 | CVPR2018 | | MobileNetV2 | 74.7 | - | CVPR2018 | | IGCV2 | 70.07 | - | CVPR2018 | | hier | 79.7 | 94.8 | ICLR2018 | | PNasNet | 82.9 | 96.2 | ECCV2018 | | AmoebaNet | 83.9 | 96.6 | arXiv2018 | | SENet | - | 97.749 | CVPR2018 | | ShuffleNetV2 | 81.44 | - | ECCV2018 | | IGCV3 | 72.2 | - | BMVC2018 | | MnasNet | 76.13 | 92.85 | arXiv2018 | from: 关于LeNet-5,AlexNet,VGG16,VGG19这些网络结构我们在中已经详细的解释，并且本教程中涉及的网路结构像ResNet,NIN,Inception,YOLO等也做了详细解释。本教程是对这些网络结构更详细的讨论。 ### 目标检测资源 *来源：Object Detection*
Method backbone test size VOC2007 VOC2010 VOC2012 ILSVRC 2013 MSCOCO 2015 Speed
OverFeat           24.3%    
R-CNN AlexNet   58.5% 53.7% 53.3% 31.4%    
R-CNN VGG16   66.0%          
SPP_net ZF-5   54.2%     31.84%    
DeepID-Net     64.1%     50.3%    
NoC 73.3%   68.8%          
Fast-RCNN VGG16   70.0% 68.8% 68.4%   19.7%(@[0.5-0.95]), 35.9%(@0.5)  
MR-CNN 78.2%   73.9%          
Faster-RCNN VGG16   78.8%   75.9%   21.9%(@[0.5-0.95]), 42.7%(@0.5) 198ms
Faster-RCNN ResNet101   85.6%   83.8%   37.4%(@[0.5-0.95]), 59.0%(@0.5)  
YOLO     63.4%   57.9%     45 fps
YOLO VGG-16     66.4%         21 fps
YOLOv2   448x448 78.6%   73.4%   21.6%(@[0.5-0.95]), 44.0%(@0.5) 40 fps
SSD VGG16 300x300 77.2%   75.8%   25.1%(@[0.5-0.95]), 43.1%(@0.5) 46 fps
SSD VGG16 512x512 79.8%   78.5%   28.8%(@[0.5-0.95]), 48.5%(@0.5) 19 fps
SSD ResNet101 300x300         28.0%(@[0.5-0.95]) 16 fps
SSD ResNet101 512x512         31.2%(@[0.5-0.95]) 8 fps
DSSD ResNet101 300x300         28.0%(@[0.5-0.95]) 8 fps
DSSD ResNet101 500x500         33.2%(@[0.5-0.95]) 6 fps
ION     79.2%   76.4%      
CRAFT     75.7%   71.3% 48.5%    
OHEM     78.9%   76.3%   25.5%(@[0.5-0.95]), 45.9%(@0.5)  
R-FCN ResNet50   77.4%         0.12sec(K40), 0.09sec(TitianX)
R-FCN ResNet101   79.5%         0.17sec(K40), 0.12sec(TitianX)
R-FCN(ms train) ResNet101   83.6%   82.0%   31.5%(@[0.5-0.95]), 53.2%(@0.5)  
PVANet 9.0     84.9%   84.2%     750ms(CPU), 46ms(TitianX)
RetinaNet ResNet101-FPN              
Light-Head R-CNN Xception* 800/1200         31.5%@[0.5:0.95] 95 fps
Light-Head R-CNN Xception* 700/1100         30.7%@[0.5:0.95] 102 fps
**Deep Neural Networks for Object Detection** + paper: **OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks** + arxiv: + github: + code: `https://cilvr.nyu.edu/doku.php?id=software:overfeat:start` **R-CNN** **Rich feature hierarchies for accurate object detection and semantic segmentation** + arxiv: + supp: + slides: + slides: + github: + notes: + caffe-pr(“Make R-CNN the Caffe detection example”): **Fast R-CNN** + arxiv: + slides: + github: + github(COCO-branch): + webcam demo: + notes: + notes: + github(“Fast R-CNN in MXNet”): + github: + github: + github: **A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection** + arxiv: + paper: + github(Caffe): **Faster R-CNN** **Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks** + arxiv: http://arxiv.org/abs/1506.01497 + gitxiv: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region + slides: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf + github(official, Matlab): https://github.com/ShaoqingRen/faster_rcnn + github: https://github.com/rbgirshick/py-faster-rcnn + github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn + github: https://github.com//jwyang/faster-rcnn.pytorch + github: https://github.com/mitmul/chainer-faster-rcnn + github: https://github.com/andreaskoepf/faster-rcnn.torch + github: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch + github: https://github.com/smallcorgi/Faster-RCNN_TF + github: https://github.com/CharlesShang/TFFRCNN + github(C++ demo): https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus + github: https://github.com/yhenon/keras-frcnn + github: https://github.com/Eniac-Xie/faster-rcnn-resnet + github(C++): https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev **R-CNN minus R** + arxiv: http://arxiv.org/abs/1506.06981 **Faster R-CNN in MXNet with distributed implementation and data parallelization** + github: https://github.com/dmlc/mxnet/tree/master/example/rcnn **Contextual Priming and Feedback for Faster R-CNN** + paper: http://abhinavsh.info/context_priming_feedback.pdf + poster: http://www.eccv2016.org/files/posters/P-1A-20.pdf **An Implementation of Faster RCNN with Study for Region Sampling** + arxiv: https://arxiv.org/abs/1702.02138 + github: https://github.com/endernewton/tf-faster-rcnn **Interpretable R-CNN** + arxiv: https://arxiv.org/abs/1711.05226 **Light-Head R-CNN** **Light-Head R-CNN: In Defense of Two-Stage Object Detector** + arxiv: https://arxiv.org/abs/1711.07264 + github(official, Tensorflow): https://github.com/zengarden/light_head_rcnn + github: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784 **Cascade R-CNN** **Cascade R-CNN: Delving into High Quality Object Detection** + arxiv: https://arxiv.org/abs/1712.00726 + github(Caffe, official): https://github.com/zhaoweicai/cascade-rcnn **MultiBox** **Scalable Object Detection using Deep Neural Networks** + arxiv: http://arxiv.org/abs/1312.2249 + github: https://github.com/google/multibox + blog: https://research.googleblog.com/2014/12/high-quality-object-detection-at-scale.html **Scalable, High-Quality Object Detection** + arxiv: http://arxiv.org/abs/1412.1441 + github: https://github.com/google/multibox **SPP-Net** **Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition** + arxiv: http://arxiv.org/abs/1406.4729 + github: https://github.com/ShaoqingRen/SPP_net + notes: http://zhangliliang.com/2014/09/13/paper-note-sppnet/ **DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection** + intro: an extension of R-CNN. box pre-training, cascade on region proposals, deformation layers and context representations + project page: http://www.ee.cuhk.edu.hk/%CB%9Cwlouyang/projects/imagenetDeepId/index.html + arxiv: http://arxiv.org/abs/1412.5661 **Object Detectors Emerge in Deep Scene CNNs** + arxiv: http://arxiv.org/abs/1412.6856 + paper: https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou_iclr15.pdf + paper: https://people.csail.mit.edu/khosla/papers/iclr2015_zhou.pdf + slides: http://places.csail.mit.edu/slide_iclr2015.pdf **segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection** + intro: CVPR 2015 + project(code+data): https://www.cs.toronto.edu/~yukun/segdeepm.html + arxiv: https://arxiv.org/abs/1502.04275 + github: https://github.com/YknZhu/segDeepM **Object Detection Networks on Convolutional Feature Maps** + intro: TPAMI 2015 + keywords: NoC + arxiv: http://arxiv.org/abs/1504.06066 **Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction** + arxiv: http://arxiv.org/abs/1504.03293 + slides: http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf + github: https://github.com/YutingZhang/fgs-obj **DeepBox: Learning Objectness with Convolutional Networks** + keywords: DeepBox + arxiv: http://arxiv.org/abs/1505.02146 + github: https://github.com/weichengkuo/DeepBox **MR-CNN** **Object detection via a multi-region & semantic segmentation-aware CNN model** + intro: ICCV 2015. MR-CNN + arxiv: http://arxiv.org/abs/1505.01749 + github: https://github.com/gidariss/mrcnn-object-detection + notes: http://zhangliliang.com/2015/05/17/paper-note-ms-cnn/ + notes: http://blog.cvmarcher.com/posts/2015/05/17/multi-region-semantic-segmentation-aware-cnn/ **YOLO** **You Only Look Once: Unified, Real-Time Object Detection** + arxiv: http://arxiv.org/abs/1506.02640 + code: http://pjreddie.com/darknet/yolo/ + github: https://github.com/pjreddie/darknet + blog: https://pjreddie.com/publications/yolo/ + slides: https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p + reddit: https://www.reddit.com/r/MachineLearning/comments/3a3m0o/realtime_object_detection_with_yolo/ + github: https://github.com/gliese581gg/YOLO_tensorflow + github: https://github.com/xingwangsfu/caffe-yolo + github: https://github.com/frankzhangrui/Darknet-Yolo + github: https://github.com/BriSkyHekun/py-darknet-yolo + github: https://github.com/tommy-qichang/yolo.torch + github: https://github.com/frischzenger/yolo-windows + github: https://github.com/AlexeyAB/yolo-windows + github: https://github.com/nilboy/tensorflow-yolo **darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++** + blog: https://thtrieu.github.io/notes/yolo-tensorflow-graph-buffer-cpp + github: https://github.com/thtrieu/darkflow **Start Training YOLO with Our Own Data** + intro: train with customized data and class numbers/labels. Linux / Windows version for darknet. + blog: http://guanghan.info/blog/en/my-works/train-yolo/ + github: https://github.com/Guanghan/darknet **YOLO: Core ML versus MPSNNGraph** + intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API. + blog: http://machinethink.net/blog/yolo-coreml-versus-mps-graph/ + github: https://github.com/hollance/YOLO-CoreML-MPSNNGraph **TensorFlow YOLO object detection on Android** + intro: Real-time object detection on Android using the YOLO network with TensorFlow + github: https://github.com/natanielruiz/android-yolo **Computer Vision in iOS – Object Detection** + blog: https://sriraghu.com/2017/07/12/computer-vision-in-ios-object-detection/ + github:https://github.com/r4ghu/iOS-CoreML-Yolo **YOLOv2** **YOLO9000: Better, Faster, Stronger** + arxiv: https://arxiv.org/abs/1612.08242 + code: http://pjreddie.com/yolo9000/ + github(Chainer): https://github.com/leetenki/YOLOv2 + github(Keras): https://github.com/allanzelener/YAD2K + github(PyTorch): https://github.com/longcw/yolo2-pytorch + github(Tensorflow): https://github.com/hizhangp/yolo_tensorflow + github(Windows): https://github.com/AlexeyAB/darknet + github: https://github.com/choasUp/caffe-yolo9000 + github: https://github.com/philipperemy/yolo-9000 **darknet_scripts** + intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors? + github: https://github.com/Jumabek/darknet_scripts **Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2** + github: https://github.com/AlexeyAB/Yolo_mark **LightNet: Bringing pjreddie’s DarkNet out of the shadows** + https://github.com//explosion/lightnet **YOLO v2 Bounding Box Tool** + intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires. + github: https://github.com/Cartucho/yolo-boundingbox-labeler-GUI **YOLOv3** **YOLOv3: An Incremental Improvement** + project page: https://pjreddie.com/darknet/yolo/ + paper: https://pjreddie.com/media/files/papers/YOLOv3.pdf + arxiv: https://arxiv.org/abs/1804.02767 + githb: https://github.com/DeNA/PyTorch_YOLOv3 + github: https://github.com/eriklindernoren/PyTorch-YOLOv3 **YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers** + https://arxiv.org/abs/1811.05588 **AttentionNet: Aggregating Weak Directions for Accurate Object Detection** + intro: ICCV 2015 + intro: state-of-the-art performance of 65% (AP) on PASCAL VOC 2007/2012 human detection task + arxiv: http://arxiv.org/abs/1506.07704 + slides: https://www.robots.ox.ac.uk/~vgg/rg/slides/AttentionNet.pdf + slides: http://image-net.org/challenges/talks/lunit-kaist-slide.pdf **DenseBox** **DenseBox: Unifying Landmark Localization with End to End Object Detection** + arxiv: http://arxiv.org/abs/1509.04874 + demo: http://pan.baidu.com/s/1mgoWWsS + KITTI result: http://www.cvlibs.net/datasets/kitti/eval_object.php **SSD** **SSD: Single Shot MultiBox Detector** + intro: ECCV 2016 Oral + arxiv: http://arxiv.org/abs/1512.02325 + paper: http://www.cs.unc.edu/~wliu/papers/ssd.pdf + slides: http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf + github(Official): https://github.com/weiliu89/caffe/tree/ssd + video: http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973 + github: https://github.com/zhreshold/mxnet-ssd + github: https://github.com/zhreshold/mxnet-ssd.cpp + github: https://github.com/rykov8/ssd_keras + github: https://github.com/balancap/SSD-Tensorflow + github: https://github.com/amdegroot/ssd.pytorch + github(Caffe): https://github.com/chuanqi305/MobileNet-SSD **What’s the diffience in performance between this new code you pushed and the previous code? #327** + https://github.com/weiliu89/caffe/issues/327 **DSSD** **DSSD : Deconvolutional Single Shot Detector** + intro: UNC Chapel Hill & Amazon Inc + arxiv: https://arxiv.org/abs/1701.06659 + github: https://github.com/chengyangfu/caffe/tree/dssd + github: https://github.com/MTCloudVision/mxnet-dssd + demo: http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd_lalaland.mp4 **Enhancement of SSD by concatenating feature maps for object detection** + intro: rainbow SSD (R-SSD) + arxiv: https://arxiv.org/abs/1705.09587 **Context-aware Single-Shot Detector** **keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs)** + arxiv: https://arxiv.org/abs/1707.08682 **Feature-Fused SSD: Fast Detection for Small Objects** + https://arxiv.org/abs/1709.05054 **FSSD** **FSSD: Feature Fusion Single Shot Multibox Detector** + https://arxiv.org/abs/1712.00960 **Weaving Multi-scale Context for Single Shot Detector** + intro: WeaveNet + keywords: fuse multi-scale information + arxiv: https://arxiv.org/abs/1712.03149 **ESSD** **Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network** + https://arxiv.org/abs/1801.05918 **Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection** + https://arxiv.org/abs/1802.06488 **MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects** + intro: Zhengzhou University + arxiv: https://arxiv.org/abs/1805.07009 **Inside-Outside Net (ION)** **Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks** + intro: “0.8s per image on a Titan X GPU (excluding proposal generation) without two-stage bounding-box regression and 1.15s per image with it”. + arxiv: http://arxiv.org/abs/1512.04143 + slides: http://www.seanbell.ca/tmp/ion-coco-talk-bell2015.pdf + coco-leaderboard: http://mscoco.org/dataset/#detections-leaderboard **Adaptive Object Detection Using Adjacency and Zoom Prediction** + intro: CVPR 2016. AZ-Net + arxiv: http://arxiv.org/abs/1512.07711 + github: https://github.com/luyongxi/az-net + youtube: https://www.youtube.com/watch?v=YmFtuNwxaNM **G-CNN: an Iterative Grid Based Object Detector** + arxiv: http://arxiv.org/abs/1512.07729 **Factors in Finetuning Deep Model for object detection** **Factors in Finetuning Deep Model for Object Detection with Long-tail Distribution** + intro: CVPR 2016.rank 3rd for provided data and 2nd for external data on ILSVRC 2015 object detection + project page: http://www.ee.cuhk.edu.hk/~wlouyang/projects/ImageNetFactors/CVPR16.html + arxiv: http://arxiv.org/abs/1601.05150 **We don’t need no bounding-boxes: Training object class detectors using only human verification** + arxiv: http://arxiv.org/abs/1602.08405 **HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection** + arxiv: http://arxiv.org/abs/1604.00600 **A MultiPath Network for Object Detection** + intro: BMVC 2016. Facebook AI Research (FAIR) + arxiv: http://arxiv.org/abs/1604.02135 + github: https://github.com/facebookresearch/multipathnet **CRAFT** **CRAFT Objects from Images** + intro: CVPR 2016. Cascade Region-proposal-network And FasT-rcnn. an extension of Faster R-CNN + project page: http://byangderek.github.io/projects/craft.html + arxiv: https://arxiv.org/abs/1604.03239 + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Yang_CRAFT_Objects_From_CVPR_2016_paper.pdf + github: https://github.com/byangderek/CRAFT **OHEM** **Training Region-based Object Detectors with Online Hard Example Mining** + intro: CVPR 2016 Oral. Online hard example mining (OHEM) + arxiv: http://arxiv.org/abs/1604.03540 + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shrivastava_Training_Region-Based_Object_CVPR_2016_paper.pdf + github(Official): https://github.com/abhi2610/ohem + author page: http://abhinav-shrivastava.info/ **S-OHEM: Stratified Online Hard Example Mining for Object Detection** + https://arxiv.org/abs/1705.02233 **Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers** + intro: CVPR 2016 + keywords: scale-dependent pooling (SDP), cascaded rejection classifiers (CRC) + paper: http://www-personal.umich.edu/~wgchoi/SDP-CRC_camready.pdf **R-FCN** **R-FCN: Object Detection via Region-based Fully Convolutional Networks** + arxiv: http://arxiv.org/abs/1605.06409 + github: https://github.com/daijifeng001/R-FCN + github(MXNet): https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn + github: https://github.com/Orpine/py-R-FCN + github: https://github.com/PureDiors/pytorch_RFCN + github: https://github.com/bharatsingh430/py-R-FCN-multiGPU + github: https://github.com/xdever/RFCN-tensorflow **R-FCN-3000 at 30fps: Decoupling Detection and Classification** + https://arxiv.org/abs/1712.01802 **Recycle deep features for better object detection** + arxiv: http://arxiv.org/abs/1607.05066 **MS-CNN** **A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection** + intro: ECCV 2016 + intro: 640×480: 15 fps, 960×720: 8 fps + arxiv: http://arxiv.org/abs/1607.07155 + github: https://github.com/zhaoweicai/mscnn + poster: http://www.eccv2016.org/files/posters/P-2B-38.pdf **Multi-stage Object Detection with Group Recursive Learning** + intro: VOC2007: 78.6%, VOC2012: 74.9% + arxiv: http://arxiv.org/abs/1608.05159 **Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection** + intro: WACV 2017. SubCNN + arxiv: http://arxiv.org/abs/1604.04693 + github: https://github.com/tanshen/SubCNN **PVANET** **PVANet: Lightweight Deep Neural Networks for Real-time Object Detection** + intro: Presented at NIPS 2016 Workshop on Efficient Methods for Deep Neural Networks (EMDNN). + Continuation of arXiv:1608.08021 + arxiv: https://arxiv.org/abs/1611.08588 + github: https://github.com/sanghoon/pva-faster-rcnn + leaderboard(PVANet 9.0): http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4 **GBD-Net** **Gated Bi-directional CNN for Object Detection** + intro: The Chinese University of Hong Kong & Sensetime Group Limited + paper: http://link.springer.com/chapter/10.1007/978-3-319-46478-7_22 + mirror: https://pan.baidu.com/s/1dFohO7v **Crafting GBD-Net for Object Detection** + intro: winner of the ImageNet object detection challenge of 2016. CUImage and CUVideo + intro: gated bi-directional CNN (GBD-Net) + arxiv: https://arxiv.org/abs/1610.02579 + github: https://github.com/craftGBD/craftGBD **StuffNet: Using ‘Stuff’ to Improve Object Detection** + arxiv: https://arxiv.org/abs/1610.05861 **Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene** + arxiv: https://arxiv.org/abs/1610.09609 **Hierarchical Object Detection with Deep Reinforcement Learning** + intro: Deep Reinforcement Learning Workshop (NIPS 2016) + project page: https://imatge-upc.github.io/detection-2016-nipsws/ + arxiv: https://arxiv.org/abs/1611.03718 + slides: http://www.slideshare.net/xavigiro/ **hierarchical-object-detection-with-deep-reinforcement-learning** + github: https://github.com/imatge-upc/detection-2016-nipsws + blog: http://jorditorres.org/nips/ **Learning to detect and localize many objects from few examples** + arxiv: https://arxiv.org/abs/1611.05664 **Speed/accuracy trade-offs for modern convolutional object detectors** + intro: CVPR 2017. Google Research + arxiv: https://arxiv.org/abs/1611.10012 **SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving** + arxiv: https://arxiv.org/abs/1612.01051 + github: https://github.com/BichenWuUCB/squeezeDet + github: https://github.com/fregu856/2D_detection **Feature Pyramid Network (FPN)** **Feature Pyramid Networks for Object Detection** + intro: Facebook AI Research + arxiv: https://arxiv.org/abs/1612.03144 **Action-Driven Object Detection with Top-Down Visual Attentions** + arxiv: https://arxiv.org/abs/1612.06704 **Beyond Skip Connections: Top-Down Modulation for Object Detection** + intro: CMU & UC Berkeley & Google Research + arxiv: https://arxiv.org/abs/1612.06851 **Wide-Residual-Inception Networks for Real-time Object Detection** + intro: Inha University + arxiv: https://arxiv.org/abs/1702.01243 **Attentional Network for Visual Object Detection** + intro: University of Maryland & Mitsubishi Electric Research Laboratories + arxiv: https://arxiv.org/abs/1702.01478 **Learning Chained Deep Features and Classifiers for Cascade in Object Detection** + keykwords: CC-Net + intro: chained cascade network (CC-Net). 81.1% mAP on PASCAL VOC 2007 + arxiv: https://arxiv.org/abs/1702.07054 **DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling** + intro: ICCV 2017 (poster) + arxiv: https://arxiv.org/abs/1703.10295 **Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries** + intro: CVPR 2017 + arxiv: https://arxiv.org/abs/1704.03944 **Spatial Memory for Context Reasoning in Object Detection** + arxiv: https://arxiv.org/abs/1704.04224 **Accurate Single Stage Detector Using Recurrent Rolling Convolution** + intro: CVPR 2017. SenseTime + keywords: Recurrent Rolling Convolution (RRC) + arxiv: https://arxiv.org/abs/1704.05776 + github: https://github.com/xiaohaoChen/rrc_detection **Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection** + https://arxiv.org/abs/1704.05775 **LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems** + intro: Embedded Vision Workshop in CVPR. UC San Diego & Qualcomm Inc + arxiv: https://arxiv.org/abs/1705.05922 **Point Linking Network for Object Detection** + intro: Point Linking Network (PLN) + arxiv: https://arxiv.org/abs/1706.03646 **Perceptual Generative Adversarial Networks for Small Object Detection** + https://arxiv.org/abs/1706.05274 **Few-shot Object Detection** + https://arxiv.org/abs/1706.08249 **Yes-Net: An effective Detector Based on Global Information** + https://arxiv.org/abs/1706.09180 **SMC Faster R-CNN: Toward a scene-specialized multi-object detector** + https://arxiv.org/abs/1706.10217 **Towards lightweight convolutional neural networks for object detection** + https://arxiv.org/abs/1707.01395 **RON: Reverse Connection with Objectness Prior Networks for Object Detection** + intro: CVPR 2017 + arxiv: https://arxiv.org/abs/1707.01691 + github: https://github.com/taokong/RON **Mimicking Very Efficient Network for Object Detection** + intro: CVPR 2017. SenseTime & Beihang University + paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Mimicking_Very_Efficient_CVPR_2017_paper.pdf **Residual Features and Unified Prediction Network for Single Stage Detection** + https://arxiv.org/abs/1707.05031 **Deformable Part-based Fully Convolutional Network for Object Detection** + intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC + arxiv: https://arxiv.org/abs/1707.06175 **Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors** + intro: ICCV 2017 + arxiv: https://arxiv.org/abs/1707.06399 **Recurrent Scale Approximation for Object Detection in CNN** + intro: ICCV 2017 + keywords: Recurrent Scale Approximation (RSA) + arxiv: https://arxiv.org/abs/1707.09531 + github: https://github.com/sciencefans/RSA-for-object-detection **DSOD** **DSOD: Learning Deeply Supervised Object Detectors from Scratch** + intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China + arxiv: https://arxiv.org/abs/1708.01241 + github: https://github.com/szq0214/DSOD **Object Detection from Scratch with Deep Supervision** + https://arxiv.org/abs/1809.09294 **RetinaNet** **Focal Loss for Dense Object Detection** + intro: ICCV 2017 Best student paper award. Facebook AI Research + keywords: RetinaNet + arxiv: https://arxiv.org/abs/1708.02002 **Focal Loss Dense Detector for Vehicle Surveillance** + https://arxiv.org/abs/1803.01114 **CoupleNet: Coupling Global Structure with Local Parts for Object Detection** + intro: ICCV 2017 + arxiv: https://arxiv.org/abs/1708.02863 **Incremental Learning of Object Detectors without Catastrophic Forgetting** + intro: ICCV 2017. Inria + arxiv: https://arxiv.org/abs/1708.06977 **Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection** + https://arxiv.org/abs/1709.04347 **StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection** + https://arxiv.org/abs/1709.05788 **Dynamic Zoom-in Network for Fast Object Detection in Large Images** + https://arxiv.org/abs/1711.05187 **Zero-Annotation Object Detection with Web Knowledge Transfer** + intro: NTU, Singapore & Amazon + keywords: multi-instance multi-label domain adaption learning framework + arxiv: https://arxiv.org/abs/1711.05954 **MegDet** **MegDet: A Large Mini-Batch Object Detector** + intro: Peking University & Tsinghua University & Megvii Inc + arxiv: https://arxiv.org/abs/1711.07240 **Single-Shot Refinement Neural Network for Object Detection** + arxiv: https://arxiv.org/abs/1711.06897 + github: https://github.com/sfzhang15/RefineDet + github: https://github.com/MTCloudVision/RefineDet-Mxnet **Receptive Field Block Net for Accurate and Fast Object Detection** + intro: RFBNet + arxiv: https://arxiv.org/abs/1711.07767 + github: https://github.com//ruinmessi/RFBNet **An Analysis of Scale Invariance in Object Detection - SNIP** + intro: CVPR 2018 + arxiv: https://arxiv.org/abs/1711.08189 + github: https://github.com/bharatsingh430/snip **Feature Selective Networks for Object Detection** + https://arxiv.org/abs/1711.08879 **Learning a Rotation Invariant Detector with Rotatable Bounding Box** + arxiv: https://arxiv.org/abs/1711.09405 + github(official, Caffe): https://github.com/liulei01/DRBox **Scalable Object Detection for Stylized Objects** + intro: Microsoft AI & Research Munich + arxiv: https://arxiv.org/abs/1711.09822 **Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids** + arxiv: https://arxiv.org/abs/1712.00886 + github: https://github.com/szq0214/GRP-DSOD **Deep Regionlets for Object Detection** + keywords: region selection network, gating network + arxiv: https://arxiv.org/abs/1712.02408 **Training and Testing Object Detectors with Virtual Images** + intro: IEEE/CAA Journal of Automatica Sinica + arxiv: https://arxiv.org/abs/1712.08470 **Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video** + keywords: object mining, object tracking, unsupervised object discovery by appearance-based clustering, self-supervised detector adaptation + arxiv: https://arxiv.org/abs/1712.08832 **Spot the Difference by Object Detection** + intro: Tsinghua University & JD Group + arxiv: https://arxiv.org/abs/1801.01051 **Localization-Aware Active Learning for Object Detection** + arxiv: https://arxiv.org/abs/1801.05124 **Object Detection with Mask-based Feature Encoding** + https://arxiv.org/abs/1802.03934 **LSTD: A Low-Shot Transfer Detector for Object Detection** + intro: AAAI 2018 + arxiv: https://arxiv.org/abs/1803.01529 **Domain Adaptive Faster R-CNN for Object Detection in the Wild** + intro: CVPR 2018. ETH Zurich & ESAT/PSI + arxiv: https://arxiv.org/abs/1803.03243 + github(official. Caffe): https://github.com/yuhuayc/da-faster-rcnn **Pseudo Mask Augmented Object Detection** + https://arxiv.org/abs/1803.05858 **Revisiting RCNN: On Awakening the Classification Power of Faster RCNN** + intro: ECCV 2018 + keywords: DCR V1 + arxiv: https://arxiv.org/abs/1803.06799 + github(official, MXNet): https://github.com/bowenc0221/ **Decoupled-Classification-Refinement** **Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection** + keywords: DCR V2 + arxiv: https://arxiv.org/abs/1810.04002 + github(official, MXNet): https://github.com/bowenc0221/Decoupled-Classification-Refinement **Learning Region Features for Object Detection** + intro: Peking University & MSRA + arxiv: https://arxiv.org/abs/1803.07066 **Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection** + intro: Singapore Management University & Zhejiang University + arxiv: https://arxiv.org/abs/1803.08208 **Object Detection for Comics using Manga109 Annotations** + intro: University of Tokyo & National Institute of Informatics, Japan + arxiv: https://arxiv.org/abs/1803.08670 **Task-Driven Super Resolution: Object Detection in Low-resolution Images** + https://arxiv.org/abs/1803.11316 **Transferring Common-Sense Knowledge for Object Detection** + https://arxiv.org/abs/1804.01077 **Multi-scale Location-aware Kernel Representation for Object Detection** + intro: CVPR 2018 + arxiv: https://arxiv.org/abs/1804.00428 + github: https://github.com/Hwang64/MLKP **Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors** + intro: National University of Defense Technology + arxiv: https://arxiv.org/abs/1804.04606 **DetNet: A Backbone network for Object Detection** + intro: Tsinghua University & Megvii Inc + arxiv: https://arxiv.org/abs/1804.06215 **Robust Physical Adversarial Attack on Faster R-CNN Object Detector** + https://arxiv.org/abs/1804.05810 **AdvDetPatch: Attacking Object Detectors with Adversarial Patches** + https://arxiv.org/abs/1806.02299 **Attacking Object Detectors via Imperceptible Patches on Background** + https://arxiv.org/abs/1809.05966 **Physical Adversarial Examples for Object Detectors** + intro: WOOT 2018 + arxiv: https://arxiv.org/abs/1807.07769 **Quantization Mimic: Towards Very Tiny CNN for Object Detection** + https://arxiv.org/abs/1805.02152 **Object detection at 200 Frames Per Second** + intro: United Technologies Research Center-Ireland + arxiv: https://arxiv.org/abs/1805.06361 **Object Detection using Domain Randomization and Generative Adversarial Refinement of Synthetic Images** + intro: CVPR 2018 Deep Vision Workshop + arxiv: https://arxiv.org/abs/1805.11778 **SNIPER: Efficient Multi-Scale Training** + intro: University of Maryland + keywords: SNIPER (Scale Normalization for Image Pyramid with Efficient Resampling) + arxiv: https://arxiv.org/abs/1805.09300 + github: https://github.com/mahyarnajibi/SNIPER **Soft Sampling for Robust Object Detection** + https://arxiv.org/abs/1806.06986 **MetaAnchor: Learning to Detect Objects with Customized Anchors** + intro: Megvii Inc (Face++) & Fudan University + arxiv: https://arxiv.org/abs/1807.00980 **Localization Recall Precision (LRP): A New Performance Metric for Object Detection** + intro: ECCV 2018. Middle East Technical University + arxiv: https://arxiv.org/abs/1807.01696 + github: https://github.com/cancam/LRP **Auto-Context R-CNN** + intro: Rejected by ECCV18 + arxiv: https://arxiv.org/abs/1807.02842 **Pooling Pyramid Network for Object Detection** + intro: Google AI Perception + arxiv: https://arxiv.org/abs/1807.03284 **Modeling Visual Context is Key to Augmenting Object Detection Datasets** + intro: ECCV 2018 + arxiv: https://arxiv.org/abs/1807.07428 **Dual Refinement Network for Single-Shot Object Detection** + https://arxiv.org/abs/1807.08638 **Acquisition of Localization Confidence for Accurate Object Detection** + intro: ECCV 2018 + arxiv: https://arxiv.org/abs/1807.11590 + gihtub: https://github.com/vacancy/PreciseRoIPooling **CornerNet: Detecting Objects as Paired Keypoints** + intro: ECCV 2018 + keywords: IoU-Net, PreciseRoIPooling + arxiv: https://arxiv.org/abs/1808.01244 + github: https://github.com/umich-vl/CornerNet **Unsupervised Hard Example Mining from Videos for Improved Object Detection** + intro: ECCV 2018 + arxiv: https://arxiv.org/abs/1808.04285 **SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection** + https://arxiv.org/abs/1808.04974 **A Survey of Modern Object Detection Literature using Deep Learning** + https://arxiv.org/abs/1808.07256 **Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages** + intro: BMVC 2018 + arxiv: https://arxiv.org/abs/1807.11013 + github: https://github.com/lyxok1/Tiny-DSOD **Deep Feature Pyramid Reconfiguration for Object Detection** + intro: ECCV 2018 + arxiv: https://arxiv.org/abs/1808.07993 **MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection** + intro: ICPR 2018 + arxiv: https://arxiv.org/abs/1809.01791 **Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks** + https://arxiv.org/abs/1809.03193 **Deep Learning for Generic Object Detection: A Survey** + https://arxiv.org/abs/1809.02165 **Training Confidence-Calibrated Classifier for Detecting Out-of-Distribution Samples** + intro: ICLR 2018 + arxiv: https://github.com/alinlab/Confident_classifier **ScratchDet:Exploring to Train Single-Shot Object Detectors from Scratch** + arxiv: https://arxiv.org/abs/1810.08425 + github: https://github.com/KimSoybean/ScratchDet **Fast and accurate object detection in high resolution 4K and 8K video using GPUs** + intro: Best Paper Finalist at IEEE High Performance Extreme Computing Conference (HPEC) 2018 + intro: Carnegie Mellon University + arxiv: https://arxiv.org/abs/1810.10551 **Hybrid Knowledge Routed Modules for Large-scale Object Detection** + intro: NIPS 2018 + arxiv: https://arxiv.org/abs/1810.12681 + github(official, PyTorch): https://github.com/chanyn/HKRM **Gradient Harmonized Single-stage Detector** + intro: AAAI 2019 + arxiv: https://arxiv.org/abs/1811.05181 **M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network** + intro: AAAI 2019 + arxiv: https://arxiv.org/abs/1811.04533 + github: https://github.com/qijiezhao/M2Det **BAN: Focusing on Boundary Context for Object Detection** + https://arxiv.org/abs/1811.05243 **Multi-layer Pruning Framework for Compressing Single Shot MultiBox Detector** + intro: WACV 2019 + arxiv: https://arxiv.org/abs/1811.08342 **R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy** + arxiv: https://arxiv.org/abs/1811.07126 + github: https://github.com/DetectionTeamUCAS/R2CNN-Plus-Plus_Tensorflow **DeRPN: Taking a further step toward more general object detection** + intro: AAAI 2019 + intro: South China University of Technology + ariv: https://arxiv.org/abs/1811.06700 + github: https://github.com/HCIILAB/DeRPN **Fast Efficient Object Detection Using Selective Attention** + https://arxiv.org/abs/1811.07502 **Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects** + https://arxiv.org/abs/1811.10862 **Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects** + https://arxiv.org/abs/1811.12152 **Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection** + https://arxiv.org/abs/1811.11318 **Grid R-CNN** + intro: SenseTime + arxiv: https://arxiv.org/abs/1811.12030 **Transferable Adversarial Attacks for Image and Video Object Detection** + https://arxiv.org/abs/1811.12641 **Anchor Box Optimization for Object Detection** + intro: University of Illinois at Urbana-Champaign & Microsoft Research + arxiv: https://arxiv.org/abs/1812.00469 **AutoFocus: Efficient Multi-Scale Inference** + intro: University of Maryland + arxiv: https://arxiv.org/abs/1812.01600 **Few-shot Object Detection via Feature Reweighting** + https://arxiv.org/abs/1812.01866 + Practical Adversarial Attack Against Object Detector + https://arxiv.org/abs/1812.10217 **Learning Efficient Detector with Semi-supervised Adaptive Distillation** + intro: SenseTime Research + arxiv: https://arxiv.org/abs/1901.00366 + github: https://github.com/Tangshitao/Semi-supervised-Adaptive-Distillation **Scale-Aware Trident Networks for Object Detection** + intro: University of Chinese Academy of Sciences & TuSimple + arxiv: https://arxiv.org/abs/1901.01892 + github: https://github.com/TuSimple/simpledet **Region Proposal by Guided Anchoring** + intro: CUHK - SenseTime Joint Lab & Amazon Rekognition & Nanyang Technological University + arxiv: https://arxiv.org/abs/1901.03278 **Consistent Optimization for Single-Shot Object Detection** + arxiv: https://arxiv.org/abs/1901.06563 + blog: https://zhuanlan.zhihu.com/p/55416312 **Bottom-up Object Detection by Grouping Extreme and Center Points** + keywords: ExtremeNet + arxiv: https://arxiv.org/abs/1901.08043 + github: https://github.com/xingyizhou/ExtremeNet **A Single-shot Object Detector with Feature Aggragation and Enhancement** + https://arxiv.org/abs/1902.02923 **Bag of Freebies for Training Object Detection Neural Networks** + intro: Amazon Web Services + arxiv: https://arxiv.org/abs/1902.04103 **Non-Maximum Suppression (NMS) End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression** + intro: CVPR 2015 + arxiv: http://arxiv.org/abs/1411.5309 + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wan_End-to-End_Integration_of_2015_CVPR_paper.pdf **A convnet for non-maximum suppression** + arxiv: http://arxiv.org/abs/1511.06437 **Soft-NMS – Improving Object Detection With One Line of Code** + intro: ICCV 2017. University of Maryland + keywords: Soft-NMS + arxiv: https://arxiv.org/abs/1704.04503 + github: https://github.com/bharatsingh430/soft-nms **Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection** + intro: CMU & Megvii Inc. (Face++) + arxiv: https://arxiv.org/abs/1809.08545 + github: https://github.com/yihui-he/softer-NMS **Learning non-maximum suppression** + intro: CVPR 2017 + project page: https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/object-recognition-and-scene-understanding/learning-nms/ + arxiv: https://arxiv.org/abs/1705.02950 + github: https://github.com/hosang/gossipnet **Relation Networks for Object Detection** + intro: CVPR 2018 oral + arxiv: https://arxiv.org/abs/1711.11575 + github(official, MXNet): https://github.com/msracver/Relation-Networks-for-Object-Detection **Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes** + keywords: Pairwise-NMS + arxiv: https://arxiv.org/abs/1901.03796 **Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples** + https://arxiv.org/abs/1902.02067 **Adversarial Examples that Fool Detectors** + intro: University of Illinois + arxiv: https://arxiv.org/abs/1712.02494 **Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods** + project page: http://nicholas.carlini.com/code/nn_breaking_detection/ + arxiv: https://arxiv.org/abs/1705.07263 + github: https://github.com/carlini/nn_breaking_detection **Weakly Supervised Object Detection Track and Transfer: Watching Videos to Simulate Strong Human Supervision for Weakly-Supervised Object Detection** + intro: CVPR 2016 + arxiv: http://arxiv.org/abs/1604.05766 **Weakly supervised object detection using pseudo-strong labels** + arxiv: http://arxiv.org/abs/1607.04731 **Saliency Guided End-to-End Learning for Weakly Supervised Object Detection** + intro: IJCAI 2017 + arxiv: https://arxiv.org/abs/1706.06768 **Visual and Semantic Knowledge Transfer for Large Scale Semi-supervised Object Detection** + intro: TPAMI 2017. National Institutes of Health (NIH) Clinical Center + arxiv: https://arxiv.org/abs/1801.03145 **Video Object Detection Learning Object Class Detectors from Weakly Annotated Video** + intro: CVPR 2012 + paper: https://www.vision.ee.ethz.ch/publications/papers/proceedings/eth_biwi_00905.pdf **Analysing domain shift factors between videos and images for object detection** + arxiv: https://arxiv.org/abs/1501.01186 **Video Object Recognition** + slides: http://vision.princeton.edu/courses/COS598/2015sp/slides/VideoRecog/Video%20Object%20Recognition.pptx **Deep Learning for Saliency Prediction in Natural Video** + intro: Submitted on 12 Jan 2016 + keywords: Deep learning, saliency map, optical flow, convolution network, contrast features + paper: https://hal.archives-ouvertes.fr/hal-01251614/document **T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos** + intro: Winning solution in ILSVRC2015 Object Detection from Video(VID) Task + arxiv: http://arxiv.org/abs/1604.02532 + github: https://github.com/myfavouritekk/T-CNN **Object Detection from Video Tubelets with Convolutional Neural Networks** + intro: CVPR 2016 Spotlight paper + arxiv: https://arxiv.org/abs/1604.04053 + paper: http://www.ee.cuhk.edu.hk/~wlouyang/Papers/KangVideoDet_CVPR16.pdf + gihtub: https://github.com/myfavouritekk/vdetlib **Object Detection in Videos with Tubelets and Multi-context Cues** + intro: SenseTime Group + slides: http://www.ee.cuhk.edu.hk/~xgwang/CUvideo.pdf + slides: http://image-net.org/challenges/talks/Object%20Detection%20in%20Videos%20with%20Tubelets%20and%20Multi-context%20Cues%20-%20Final.pdf **Context Matters: Refining Object Detection in Video with Recurrent Neural Networks** + intro: BMVC 2016 + keywords: pseudo-labeler + arxiv: http://arxiv.org/abs/1607.04648 + paper: http://vision.cornell.edu/se3/wp-content/uploads/2016/07/video_object_detection_BMVC.pdf **CNN Based Object Detection in Large Video Images** + intro: WangTao @ 爱奇艺 + keywords: object retrieval, object detection, scene classification + slides: http://on-demand.gputechconf.com/gtc/2016/presentation/s6362-wang-tao-cnn-based-object-detection-large-video-images.pdf **Object Detection in Videos with Tubelet Proposal Networks** + arxiv: https://arxiv.org/abs/1702.06355 **Flow-Guided Feature Aggregation for Video Object Detection** + intro: MSRA + arxiv: https://arxiv.org/abs/1703.10025 **Video Object Detection using Faster R-CNN** + blog: http://andrewliao11.github.io/object_detection/faster_rcnn/ + github: https://github.com/andrewliao11/py-faster-rcnn-imagenet **Improving Context Modeling for Video Object Detection and Tracking** + http://image-net.org/challenges/talks_2017/ilsvrc2017_short(poster).pdf **Temporal Dynamic Graph LSTM for Action-driven Video Object Detection** + intro: ICCV 2017 + arxiv: https://arxiv.org/abs/1708.00666 **Mobile Video Object Detection with Temporally-Aware Feature Maps** + https://arxiv.org/abs/1711.06368 **Towards High Performance Video Object Detection** + https://arxiv.org/abs/1711.11577 **Impression Network for Video Object Detection** + https://arxiv.org/abs/1712.05896 **Spatial-Temporal Memory Networks for Video Object Detection** + https://arxiv.org/abs/1712.06317 **3D-DETNet: a Single Stage Video-Based Vehicle Detector** + https://arxiv.org/abs/1801.01769 **Object Detection in Videos by Short and Long Range Object Linking** + https://arxiv.org/abs/1801.09823 **Object Detection in Video with Spatiotemporal Sampling Networks** + intro: University of Pennsylvania, 2Dartmouth College + arxiv: https://arxiv.org/abs/1803.05549 **Towards High Performance Video Object Detection for Mobiles** + intro: Microsoft Research Asia + arxiv: https://arxiv.org/abs/1804.05830 **Optimizing Video Object Detection via a Scale-Time Lattice** + intro: CVPR 2018 + project page: http://mmlab.ie.cuhk.edu.hk/projects/ST-Lattice/ + arxiv: https://arxiv.org/abs/1804.05472 + github: https://github.com/hellock/scale-time-lattice **Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing** + https://arxiv.org/abs/1809.01701 **Fast Object Detection in Compressed Video** + https://arxiv.org/abs/1811.11057 **Tube-CNN: Modeling temporal evolution of appearance for object detection in video** + intro: INRIA/ENS + arxiv: https://arxiv.org/abs/1812.02619 **AdaScale: Towards Real-time Video Object Detection Using Adaptive Scaling** + intro: SysML 2019 oral + arxiv: https://arxiv.org/abs/1902.02910 **Object Detection on Mobile Devices Pelee: A Real-Time Object Detection System on Mobile Devices** + intro: ICLR 2018 workshop track + intro: based on the SSD + arxiv: https://arxiv.org/abs/1804.06882 + github: https://github.com/Robert-JunWang/Pelee **Object Detection in 3D Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks** + arxiv: https://arxiv.org/abs/1609.06666 **Complex-YOLO: Real-time 3D Object Detection on Point Clouds** + intro: Valeo Schalter und Sensoren GmbH & Ilmenau University of Technology + arxiv: https://arxiv.org/abs/1803.06199 **Focal Loss in 3D Object Detection** + arxiv: https://arxiv.org/abs/1809.06065 + github: https://github.com/pyun-ram/FL3D **3D Object Detection Using Scale Invariant and Feature Reweighting Networks** + intro: AAAI 2019 + arxiv: https://arxiv.org/abs/1901.02237 **3D Backbone Network for 3D Object Detection** + https://arxiv.org/abs/1901.08373 **Object Detection on RGB-D Learning Rich Features from RGB-D Images for Object Detection and Segmentation** + arxiv: http://arxiv.org/abs/1407.5736 **Differential Geometry Boosts Convolutional Neural Networks for Object Detection** + intro: CVPR 2016 + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w23/html/Wang_Differential_Geometry_Boosts_CVPR_2016_paper.html **A Self-supervised Learning System for Object Detection using Physics Simulation and Multi-view Pose Estimation** + https://arxiv.org/abs/1703.03347 **Cross-Modal Attentional Context Learning for RGB-D Object Detection** + intro: IEEE Transactions on Image Processing + arxiv: https://arxiv.org/abs/1810.12829 **Zero-Shot Object Detection** **Zero-Shot Detection** + intro: Australian National University + keywords: YOLO + arxiv: https://arxiv.org/abs/1803.07113 **Zero-Shot Object Detection** + https://arxiv.org/abs/1804.04340 **Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts** + intro: Australian National University + arxiv: https://arxiv.org/abs/1803.06049 **Zero-Shot Object Detection by Hybrid Region Embedding** + intro: Middle East Technical University & Hacettepe University + arxiv: https://arxiv.org/abs/1805.06157 **Salient Object Detection This task involves predicting the salient regions of an image given by human eye fixations.** + Best Deep Saliency Detection Models (CVPR 2016 & 2015) + http://i.cs.hku.hk/~yzyu/vision.html **Large-scale optimization of hierarchical features for saliency prediction in natural images** + paper: http://coxlab.org/pdfs/cvpr2014_vig_saliency.pdf **Predicting Eye Fixations using Convolutional Neural Networks** + paper: http://www.escience.cn/system/file?fileId=72648 **Saliency Detection by Multi-Context Deep Learning** + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhao_Saliency_Detection_by_2015_CVPR_paper.pdf **DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection** + arxiv: http://arxiv.org/abs/1510.05484 **SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection** + paper: www.shengfenghe.com/supercnn-a-superpixelwise-convolutional-neural-network-for-salient-object-detection.html **Shallow and Deep Convolutional Networks for Saliency Prediction** + intro: CVPR 2016 + arxiv: http://arxiv.org/abs/1603.00845 + github: https://github.com/imatge-upc/saliency-2016-cvpr **Recurrent Attentional Networks for Saliency Detection** + intro: CVPR 2016. recurrent attentional convolutional-deconvolution network (RACDNN) + arxiv: http://arxiv.org/abs/1604.03227 **Two-Stream Convolutional Networks for Dynamic Saliency Prediction** + arxiv: http://arxiv.org/abs/1607.04730 **Unconstrained Salient Object Detection** **Unconstrained Salient Object Detection via Proposal Subset Optimization** + intro: CVPR 2016 + project page: http://cs-people.bu.edu/jmzhang/sod.html + paper: http://cs-people.bu.edu/jmzhang/SOD/CVPR16SOD_camera_ready.pdf + github: https://github.com/jimmie33/SOD + caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-object-proposal-models-for-salient-object-detection **DHSNet: Deep Hierarchical Saliency Network for Salient Object Detection** + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Liu_DHSNet_Deep_Hierarchical_CVPR_2016_paper.pdf **Salient Object Subitizing** + intro: CVPR 2015 + intro: predicting the existence and the number of salient objects in an image using holistic cues + project page: http://cs-people.bu.edu/jmzhang/sos.html + arxiv: http://arxiv.org/abs/1607.07525 + paper: http://cs-people.bu.edu/jmzhang/SOS/SOS_preprint.pdf + caffe model zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo#cnn-models-for-salient-object-subitizing **Deeply-Supervised Recurrent Convolutional Neural Network for Saliency Detection** + intro: ACMMM 2016. deeply-supervised recurrent convolutional neural network (DSRCNN) + arxiv: http://arxiv.org/abs/1608.05177 **Saliency Detection via Combining Region-Level and Pixel-Level Predictions with CNNs** + intro: ECCV 2016 + arxiv: http://arxiv.org/abs/1608.05186 **Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection** + arxiv: http://arxiv.org/abs/1608.08029 **A Deep Multi-Level Network for Saliency Prediction** + arxiv: http://arxiv.org/abs/1609.01064 **Visual Saliency Detection Based on Multiscale Deep CNN Features** + intro: IEEE Transactions on Image Processing + arxiv: http://arxiv.org/abs/1609.02077 **A Deep Spatial Contextual Long-term Recurrent Convolutional Network for Saliency Detection** + intro: DSCLRCN + arxiv: https://arxiv.org/abs/1610.01708 **Deeply supervised salient object detection with short connections** + intro: IEEE TPAMI 2018 (IEEE CVPR 2017) + arxiv: https://arxiv.org/abs/1611.04849 + github(official, Caffe): https://github.com/Andrew-Qibin/DSS + github(Tensorflow): https://github.com/Joker316701882/Salient-Object-Detection **Weakly Supervised Top-down Salient Object Detection** + intro: Nanyang Technological University + arxiv: https://arxiv.org/abs/1611.05345 **SalGAN: Visual Saliency Prediction with Generative Adversarial Networks** + project page: https://imatge-upc.github.io/saliency-salgan-2017/ + arxiv: https://arxiv.org/abs/1701.01081 **Visual Saliency Prediction Using a Mixture of Deep Neural Networks** + arxiv: https://arxiv.org/abs/1702.00372 **A Fast and Compact Salient Score Regression Network Based on Fully Convolutional Network** + arxiv: https://arxiv.org/abs/1702.00615 **Saliency Detection by Forward and Backward Cues in Deep-CNNs** + https://arxiv.org/abs/1703.00152 **Supervised Adversarial Networks for Image Saliency Detection** + https://arxiv.org/abs/1704.07242 **Group-wise Deep Co-saliency Detection** + https://arxiv.org/abs/1707.07381 **Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection** + intro: University of Maryland College Park & eBay Inc + arxiv: https://arxiv.org/abs/1708.00079 **Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection** + intro: ICCV 2017 + arixv: https://arxiv.org/abs/1708.02001 **Learning Uncertain Convolutional Features for Accurate Saliency Detection** + intro: Accepted as a poster in ICCV 2017 + arxiv: https://arxiv.org/abs/1708.02031 **Deep Edge-Aware Saliency Detection** + https://arxiv.org/abs/1708.04366 **Self-explanatory Deep Salient Object Detection** + intro: National University of Defense Technology, China & National University of Singapore + arxiv: https://arxiv.org/abs/1708.05595 **PiCANet: Learning Pixel-wise Contextual Attention in ConvNets and Its Application in Saliency Detection** + https://arxiv.org/abs/1708.06433 **DeepFeat: A Bottom Up and Top Down Saliency Model Based on Deep Features of Convolutional Neural Nets** + https://arxiv.org/abs/1709.02495 **Recurrently Aggregating Deep Features for Salient Object Detection** + intro: AAAI 2018 + paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16775/16281 **Deep saliency: What is learnt by a deep network about saliency?** + intro: 2nd Workshop on Visualisation for Deep Learning in the 34th International Conference On Machine Learning + arxiv: https://arxiv.org/abs/1801.04261 **Contrast-Oriented Deep Neural Networks for Salient Object Detection** + intro: TNNLS + arxiv: https://arxiv.org/abs/1803.11395 **Salient Object Detection by Lossless Feature Reflection** + intro: IJCAI 2018 + arxiv: https://arxiv.org/abs/1802.06527 **HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection** + https://arxiv.org/abs/1804.05142 **Video Saliency Detection Deep Learning For Video Saliency Detection** + arxiv: https://arxiv.org/abs/1702.00871 **Video Salient Object Detection Using Spatiotemporal Deep Features** + https://arxiv.org/abs/1708.01447 **Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM** + https://arxiv.org/abs/1709.06316 **Visual Relationship Detection** **Visual Relationship Detection with Language Priors** + intro: ECCV 2016 oral + paper: https://cs.stanford.edu/people/ranjaykrishna/vrd/vrd.pdf + github: https://github.com/Prof-Lu-Cewu/Visual-Relationship-Detection **ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection** **intro: Visual Phrase reasoning Convolutional Neural Network (ViP-CNN), Visual Phrase Reasoning** + Structure (VPRS) + arxiv: https://arxiv.org/abs/1702.07191 **Visual Translation Embedding Network for Visual Relation Detection** + arxiv: https://www.arxiv.org/abs/1702.08319 **Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection** + intro: CVPR 2017 spotlight paper + arxiv: https://arxiv.org/abs/1703.03054 **Detecting Visual Relationships with Deep Relational Networks** + intro: CVPR 2017 oral. The Chinese University of Hong Kong + arxiv: https://arxiv.org/abs/1704.03114 **Identifying Spatial Relations in Images using Convolutional Neural Networks** + https://arxiv.org/abs/1706.04215 **PPR-FCN: Weakly Supervised Visual Relation Detection via Parallel Pairwise R-FCN** + intro: ICCV + arxiv: https://arxiv.org/abs/1708.01956 **Natural Language Guided Visual Relationship Detection** + https://arxiv.org/abs/1711.06032 **Detecting Visual Relationships Using Box Attention** + intro: Google AI & IST Austria + arxiv: https://arxiv.org/abs/1807.02136 **Google AI Open Images - Visual Relationship Track** + intro: Detect pairs of objects in particular relationships + kaggle: https://www.kaggle.com/c/google-ai-open-images-visual-relationship-track **Context-Dependent Diffusion Network for Visual Relationship Detection** + intro: 2018 ACM Multimedia Conference + arxiv: https://arxiv.org/abs/1809.06213 **A Problem Reduction Approach for Visual Relationships Detection** + intro: ECCV 2018 Workshop + arxiv: https://arxiv.org/abs/1809.09828 **Face Deteciton Multi-view Face Detection Using Deep Convolutional Neural Networks** + intro: Yahoo + arxiv: http://arxiv.org/abs/1502.02766 + github: https://github.com/guoyilin/FaceDetection_CNN **From Facial Parts Responses to Face Detection: A Deep Learning Approach** + intro: ICCV 2015. CUHK + project page: http://personal.ie.cuhk.edu.hk/~ys014/projects/Faceness/Faceness.html + arxiv: https://arxiv.org/abs/1509.06451 + paper: http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Yang_From_Facial_Parts_ICCV_2015_paper.pdf **Compact Convolutional Neural Network Cascade for Face Detection** + arxiv: http://arxiv.org/abs/1508.01292 + github: https://github.com/Bkmz21/FD-Evaluation + github: https://github.com/Bkmz21/CompactCNNCascade **Face Detection with End-to-End Integration of a ConvNet and a 3D Model** + intro: ECCV 2016 + arxiv: https://arxiv.org/abs/1606.00850 + github(MXNet): https://github.com/tfwu/FaceDetection-ConvNet-3D **CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection** + intro: CMU + arxiv: https://arxiv.org/abs/1606.05413 **Towards a Deep Learning Framework for Unconstrained Face Detection** + intro: overlap with CMS-RCNN + arxiv: https://arxiv.org/abs/1612.05322 **Supervised Transformer Network for Efficient Face Detection** + arxiv: http://arxiv.org/abs/1607.05477 **UnitBox: An Advanced Object Detection Network** + intro: ACM MM 2016 + keywords: IOULoss + arxiv: http://arxiv.org/abs/1608.01471 **Bootstrapping Face Detection with Hard Negative Examples** + author: 万韶华 @ 小米. + intro: Faster R-CNN, hard negative mining. state-of-the-art on the FDDB dataset + arxiv: http://arxiv.org/abs/1608.02236 **Grid Loss: Detecting Occluded Faces** + intro: ECCV 2016 + arxiv: https://arxiv.org/abs/1609.00129 + paper: http://lrs.icg.tugraz.at/pubs/opitz_eccv_16.pdf + poster: http://www.eccv2016.org/files/posters/P-2A-34.pdf **A Multi-Scale Cascade Fully Convolutional Network Face Detector** + intro: ICPR 2016 + arxiv: http://arxiv.org/abs/1609.03536 **MTCNN Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks** **Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Neural Networks** + project page: https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html + arxiv: https://arxiv.org/abs/1604.02878 + github(official, Matlab): https://github.com/kpzhang93/MTCNN_face_detection_alignment + github: https://github.com/pangyupo/mxnet_mtcnn_face_detection + github: https://github.com/DaFuCoding/MTCNN_Caffe + github(MXNet): https://github.com/Seanlinx/mtcnn + github: https://github.com/Pi-DeepLearning/RaspberryPi-FaceDetection-MTCNN-Caffe-With-Motion + github(Caffe): https://github.com/foreverYoungGitHub/MTCNN + github: https://github.com/CongWeilin/mtcnn-caffe + github(OpenCV+OpenBlas): https://github.com/AlphaQi/MTCNN-light + github(Tensorflow+golang): https://github.com/jdeng/goface **Face Detection using Deep Learning: An Improved Faster RCNN Approach** + intro: DeepIR Inc + arxiv: https://arxiv.org/abs/1701.08289 **Faceness-Net: Face Detection through Deep Facial Part Responses** + intro: An extended version of ICCV 2015 paper + arxiv: https://arxiv.org/abs/1701.08393 **Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained “Hard Faces”** + intro: CVPR 2017. MP-RCNN, MP-RPN + arxiv: https://arxiv.org/abs/1703.09145 **End-To-End Face Detection and Recognition** + https://arxiv.org/abs/1703.10818 **Face R-CNN** + https://arxiv.org/abs/1706.01061 **Face Detection through Scale-Friendly Deep Convolutional Networks** + https://arxiv.org/abs/1706.02863 **Scale-Aware Face Detection** + intro: CVPR 2017. SenseTime & Tsinghua University + arxiv: https://arxiv.org/abs/1706.09876 **Detecting Faces Using Inside Cascaded Contextual CNN** + intro: CVPR 2017. Tencent AI Lab & SenseTime + paper: http://ai.tencent.com/ailab/media/publications/Detecting_Faces_Using_Inside_Cascaded_Contextual_CNN.pdf **Multi-Branch Fully Convolutional Network for Face Detection** + https://arxiv.org/abs/1707.06330 **SSH: Single Stage Headless Face Detector** + intro: ICCV 2017. University of Maryland + arxiv: https://arxiv.org/abs/1708.03979 + github(official, Caffe): https://github.com/mahyarnajibi/SSH **Dockerface: an easy to install and use Faster R-CNN face detector in a Docker container** + https://arxiv.org/abs/1708.04370 **FaceBoxes: A CPU Real-time Face Detector with High Accuracy** + intro: IJCB 2017 + keywords: Rapidly Digested Convolutional Layers (RDCL), Multiple Scale Convolutional Layers (MSCL) + intro: the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images + arxiv: https://arxiv.org/abs/1708.05234 + github(official): https://github.com/sfzhang15/FaceBoxes + github(Caffe): https://github.com/zeusees/FaceBoxes **S3FD: Single Shot Scale-invariant Face Detector** + intro: ICCV 2017. Chinese Academy of Sciences + intro: can run at 36 FPS on a Nvidia Titan X (Pascal) for VGA-resolution images + arxiv: https://arxiv.org/abs/1708.05237 + github(Caffe, official): https://github.com/sfzhang15/SFD + github: https://github.com//clcarwin/SFD_pytorch **Detecting Faces Using Region-based Fully Convolutional Networks** + https://arxiv.org/abs/1709.05256 **AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection** + https://arxiv.org/abs/1709.07326 **Face Attention Network: An effective Face Detector for the Occluded Faces** + https://arxiv.org/abs/1711.07246 **Feature Agglomeration Networks for Single Stage Face Detection** + https://arxiv.org/abs/1712.00721 **Face Detection Using Improved Faster RCNN** + intro: Huawei Cloud BU + arxiv: https://arxiv.org/abs/1802.02142 **PyramidBox: A Context-assisted Single Shot Face Detector** + intro: Baidu, Inc + arxiv: https://arxiv.org/abs/1803.07737 **A Fast Face Detection Method via Convolutional Neural Network** + intro: Neurocomputing + arxiv: https://arxiv.org/abs/1803.10103 **Beyond Trade-off: Accelerate FCN-based Face Detector with Higher Accuracy** + intro: CVPR 2018. Beihang University & CUHK & Sensetime + arxiv: https://arxiv.org/abs/1804.05197 **Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks** + intro: CVPR 2018 + arxiv: https://arxiv.org/abs/1804.06039 + github(binary library): https://github.com/Jack-CV/PCN **SFace: An Efficient Network for Face Detection in Large Scale Variations** + intro: Beihang University & Megvii Inc. (Face++) + arxiv: https://arxiv.org/abs/1804.06559 **Survey of Face Detection on Low-quality Images** + https://arxiv.org/abs/1804.07362 **Anchor Cascade for Efficient Face Detection** + intro: The University of Sydney + arxiv: https://arxiv.org/abs/1805.03363 **Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization** + intro: IEEE MMSP + arxiv: https://arxiv.org/abs/1805.12302 **Selective Refinement Network for High Performance Face Detection** + https://arxiv.org/abs/1809.02693 **DSFD: Dual Shot Face Detector** + https://arxiv.org/abs/1810.10220 **Learning Better Features for Face Detection with Feature Fusion and Segmentation Supervision** + https://arxiv.org/abs/1811.08557 **FA-RPN: Floating Region Proposals for Face Detection** + https://arxiv.org/abs/1812.05586 **Robust and High Performance Face Detector** + https://arxiv.org/abs/1901.02350 **DAFE-FD: Density Aware Feature Enrichment for Face Detection** + https://arxiv.org/abs/1901.05375 **Improved Selective Refinement Network for Face Detection** + intro: Chinese Academy of Sciences & JD AI Research + arxiv: https://arxiv.org/abs/1901.06651 **Revisiting a single-stage method for face detection** + https://arxiv.org/abs/1902.01559 **Detect Small Faces Finding Tiny Faces** + intro: CVPR 2017. CMU + project page: http://www.cs.cmu.edu/~peiyunh/tiny/index.html + arxiv: https://arxiv.org/abs/1612.04402 + github(official, Matlab): https://github.com/peiyunh/tiny + github(inference-only): https://github.com/chinakook/hr101_mxnet + github: https://github.com/cydonia999/Tiny_Faces_in_Tensorflow **Detecting and counting tiny faces** + intro: ENS Paris-Saclay. ExtendedTinyFaces + intro: Detecting and counting small objects - Analysis, review and application to counting + arxiv: https://arxiv.org/abs/1801.06504 + github: https://github.com/alexattia/ExtendedTinyFaces **Seeing Small Faces from Robust Anchor’s Perspective** + intro: CVPR 2018 + arxiv: https://arxiv.org/abs/1802.09058 **Face-MagNet: Magnifying Feature Maps to Detect Small Faces** + intro: WACV 2018 + keywords: Face Magnifier Network (Face-MageNet) + arxiv: https://arxiv.org/abs/1803.05258 + github: https://github.com/po0ya/face-magnet **Robust Face Detection via Learning Small Faces on Hard Images** + intro: Johns Hopkins University & Stanford University + arxiv: https://arxiv.org/abs/1811.11662 + github: https://github.com/bairdzhang/smallhardface **SFA: Small Faces Attention Face Detector** + intro: Jilin University + arxiv: https://arxiv.org/abs/1812.08402 **Person Head Detection Context-aware CNNs for person head detection** + intro: ICCV 2015 + project page: http://www.di.ens.fr/willow/research/headdetection/ + arxiv: http://arxiv.org/abs/1511.07917 + github: https://github.com/aosokin/cnn_head_detection **Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture** + https://arxiv.org/abs/1803.09256 **A Comparison of CNN-based Face and Head Detectors for Real-Time Video Surveillance Applications** + https://arxiv.org/abs/1809.03336 **FCHD: A fast and accurate head detector** + arxiv: https://arxiv.org/abs/1809.08766 + github(PyTorch, official): https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector **Pedestrian Detection / People Detection** **Pedestrian Detection aided by Deep Learning Semantic Tasks** + intro: CVPR 2015 + project page: http://mmlab.ie.cuhk.edu.hk/projects/TA-CNN/ + arxiv: http://arxiv.org/abs/1412.0069 **Deep Learning Strong Parts for Pedestrian Detection** + intro: ICCV 2015. CUHK. DeepParts + intro: Achieving 11.89% average miss rate on Caltech Pedestrian Dataset + paper: http://personal.ie.cuhk.edu.hk/~pluo/pdf/tianLWTiccv15.pdf **Taking a Deeper Look at Pedestrians** + intro: CVPR 2015 + arxiv: https://arxiv.org/abs/1501.05790 **Convolutional Channel Features** + intro: ICCV 2015 + arxiv: https://arxiv.org/abs/1504.07339 + github: https://github.com/byangderek/CCF **End-to-end people detection in crowded scenes** + arxiv: http://arxiv.org/abs/1506.04878 + github: https://github.com/Russell91/reinspect ipn: http://nbviewer.ipython.org/github/Russell91/ReInspect/blob/master/evaluation_reinspect.ipynb + youtube: https://www.youtube.com/watch?v=QeWl0h3kQ24 **Learning Complexity-Aware Cascades for Deep Pedestrian Detection** + intro: ICCV 2015 + arxiv: https://arxiv.org/abs/1507.05348 **Deep convolutional neural networks for pedestrian detection** + arxiv: http://arxiv.org/abs/1510.03608 + github: https://github.com/DenisTome/DeepPed **Scale-aware Fast R-CNN for Pedestrian Detection** + arxiv: https://arxiv.org/abs/1510.08160 **New algorithm improves speed and accuracy of pedestrian detection** + blog: http://www.eurekalert.org/pub_releases/2016-02/uoc–nai020516.php **Pushing the Limits of Deep CNNs for Pedestrian Detection** + intro: “set a new record on the Caltech pedestrian dataset, lowering the log-average miss rate from 11.7% to 8.9%” + arxiv: http://arxiv.org/abs/1603.04525 **A Real-Time Deep Learning Pedestrian Detector for Robot Navigation** + arxiv: http://arxiv.org/abs/1607.04436 **A Real-Time Pedestrian Detector using Deep Learning for Human-Aware Navigation** + arxiv: http://arxiv.org/abs/1607.04441 **Is Faster R-CNN Doing Well for Pedestrian Detection?** + intro: ECCV 2016 + arxiv: http://arxiv.org/abs/1607.07032 + github: https://github.com/zhangliliang/RPN_BF/tree/RPN-pedestrian **Unsupervised Deep Domain Adaptation for Pedestrian Detection** + intro: ECCV Workshop 2016 + arxiv: https://arxiv.org/abs/1802.03269 **Reduced Memory Region Based Deep Convolutional Neural Network Detection** + intro: IEEE 2016 ICCE-Berlin + arxiv: http://arxiv.org/abs/1609.02500 **Fused DNN: A deep neural network fusion approach to fast and robust pedestrian detection** + arxiv: https://arxiv.org/abs/1610.03466 **Detecting People in Artwork with CNNs** + intro: ECCV 2016 Workshops + arxiv: https://arxiv.org/abs/1610.08871 **Multispectral Deep Neural Networks for Pedestrian Detection** + intro: BMVC 2016 oral + arxiv: https://arxiv.org/abs/1611.02644 **Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection** + https://arxiv.org/abs/1902.05291 **Deep Multi-camera People Detection** + arxiv: https://arxiv.org/abs/1702.04593 **Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters** + intro: CVPR 2017 + project page: http://ml.cs.tsinghua.edu.cn:5000/publications/synunity/ + arxiv: https://arxiv.org/abs/1703.06283 + github(Tensorflow): https://github.com/huangshiyu13/RPNplus **What Can Help Pedestrian Detection?** + intro: CVPR 2017. Tsinghua University & Peking University & Megvii Inc. + keywords: Faster R-CNN, HyperLearner + arxiv: https://arxiv.org/abs/1705.02757 + paper: http://openaccess.thecvf.com/content_cvpr_2017/papers/Mao_What_Can_Help_CVPR_2017_paper.pdf **Illuminating Pedestrians via Simultaneous Detection & Segmentation** + [https://arxiv.org/abs/1706.08564](https://arxiv.org/abs/1706.08564) **Rotational Rectification Network for Robust Pedestrian Detection** + intro: CMU & Volvo Construction + arxiv: https://arxiv.org/abs/1706.08917 **STD-PD: Generating Synthetic Training Data for Pedestrian Detection in Unannotated Videos** + intro: The University of North Carolina at Chapel Hill + arxiv: https://arxiv.org/abs/1707.09100 **Too Far to See? Not Really! — Pedestrian Detection with Scale-aware Localization Policy** + https://arxiv.org/abs/1709.00235 **Repulsion Loss: Detecting Pedestrians in a Crowd** + https://arxiv.org/abs/1711.07752 **Aggregated Channels Network for Real-Time Pedestrian Detection** + https://arxiv.org/abs/1801.00476 **Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection** + intro: State Key Lab of CAD&CG, Zhejiang University + arxiv: https://arxiv.org/abs/1803.05347 **Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection** + https://arxiv.org/abs/1804.00872 **Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond** + https://arxiv.org/abs/1804.02047 **PCN: Part and Context Information for Pedestrian Detection with CNNs** + intro: British Machine Vision Conference(BMVC) 2017 + arxiv: https://arxiv.org/abs/1804.04483 **Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation** + intro: ECCV 2018. Hikvision Research Institute + arxiv: https://arxiv.org/abs/1807.01438 **Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd** + intro: ECCV 2018 + arxiv: https://arxiv.org/abs/1807.08407 **Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation** + intro: BMVC 2018 + arxiv: https://arxiv.org/abs/1808.04818 **Pedestrian Detection with Autoregressive Network Phases** + intro: Michigan State University + arxiv: https://arxiv.org/abs/1812.00440 **The Cross-Modality Disparity Problem in Multispectral Pedestrian Detection** + https://arxiv.org/abs/1901.02645 **Vehicle Detection DAVE: A Unified Framework for Fast Vehicle Detection and Annotation** + intro: ECCV 2016 + arxiv: http://arxiv.org/abs/1607.04564 **Evolving Boxes for fast Vehicle Detection** + arxiv: https://arxiv.org/abs/1702.00254 **Fine-Grained Car Detection for Visual Census Estimation** + intro: AAAI 2016 + arxiv: https://arxiv.org/abs/1709.02480 **SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection** + intro: IEEE Transactions on Intelligent Transportation Systems (T-ITS) + arxiv: https://arxiv.org/abs/1804.00433 **Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data** + intro: UC Berkeley + arxiv: https://arxiv.org/abs/1808.08603 **Domain Randomization for Scene-Specific Car Detection and Pose Estimation** + https://arxiv.org/abs/1811.05939 **ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery** + intro: ECCV 2018, UAVision 2018 + arxiv: https://arxiv.org/abs/1811.06318 **Traffic-Sign Detection Traffic-Sign Detection and Classification in the Wild** + intro: CVPR 2016 + project page(code+dataset): http://cg.cs.tsinghua.edu.cn/traffic-sign/ + paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Traffic-Sign_Detection_and_CVPR_2016_paper.pdf + code & model: http://cg.cs.tsinghua.edu.cn/traffic-sign/data_model_code/newdata0411.zip **Evaluating State-of-the-art Object Detector on Challenging Traffic Light Data** + intro: CVPR 2017 workshop + paper: http://openaccess.thecvf.com/content_cvpr_2017_workshops/w9/papers/Jensen_Evaluating_State-Of-The-Art_Object_CVPR_2017_paper.pdf **Detecting Small Signs from Large Images** + intro: IEEE Conference on Information Reuse and Integration (IRI) 2017 oral + arxiv: https://arxiv.org/abs/1706.08574 **Localized Traffic Sign Detection with Multi-scale Deconvolution Networks** + https://arxiv.org/abs/1804.10428 **Detecting Traffic Lights by Single Shot Detection** + intro: ITSC 2018 + arxiv: https://arxiv.org/abs/1805.02523 **A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection** + intro: IEEE 15th Conference on Computer and Robot Vision + arxiv: https://arxiv.org/abs/1806.07987 + demo: https://www.youtube.com/watch?v=_YmogPzBXOw&feature=youtu.be **Skeleton Detection Object Skeleton Extraction in Natural Images by Fusing Scale-associated Deep Side Outputs** + arxiv: http://arxiv.org/abs/1603.09446 + github: https://github.com/zeakey/DeepSkeleton **DeepSkeleton: Learning Multi-task Scale-associated Deep Side Outputs for Object Skeleton Extraction in Natural Images** + arxiv: http://arxiv.org/abs/1609.03659 **SRN: Side-output Residual Network for Object Symmetry Detection in the Wild** + intro: CVPR 2017 + arxiv: https://arxiv.org/abs/1703.02243 + github: https://github.com/KevinKecc/SRN **Hi-Fi: Hierarchical Feature Integration for Skeleton Detection** + https://arxiv.org/abs/1801.01849 **Fruit Detection Deep Fruit Detection in Orchards** + arxiv: https://arxiv.org/abs/1610.03677 **Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards** + intro: The Journal of Field Robotics in May 2016 + project page: http://confluence.acfr.usyd.edu.au/display/AGPub/ + arxiv: https://arxiv.org/abs/1610.08120 **Shadow Detection Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network** + https://arxiv.org/abs/1709.09283 **A+D-Net: Shadow Detection with Adversarial Shadow Attenuation** + https://arxiv.org/abs/1712.01361 **Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal** + https://arxiv.org/abs/1712.02478 **Direction-aware Spatial Context Features for Shadow Detection** + intro: CVPR 2018 + arxiv: https://arxiv.org/abs/1712.04142 **Direction-aware Spatial Context Features for Shadow Detection and Removal** + intro: The Chinese University of Hong Kong & The Hong Kong Polytechnic University + arxiv:- arxiv: https://arxiv.org/abs/1805.04635 **Others Detection Deep Deformation Network for Object Landmark Localization** + arxiv: http://arxiv.org/abs/1605.01014 **Fashion Landmark Detection in the Wild** + intro: ECCV 2016 + project page: http://personal.ie.cuhk.edu.hk/~lz013/projects/FashionLandmarks.html + arxiv: http://arxiv.org/abs/1608.03049 + github(Caffe): https://github.com/liuziwei7/fashion-landmarks **Deep Learning for Fast and Accurate Fashion Item Detection** + intro: Kuznech Inc. + intro: MultiBox and Fast R-CNN + paper: https://kddfashion2016.mybluemix.net/kddfashion_finalSubmissions/Deep%20Learning%20for%20Fast%20and%20Accurate%20Fashion%20Item%20Detection.pdf **OSMDeepOD - OSM and Deep Learning based Object Detection from Aerial Imagery (formerly known as “OSM-Crosswalk-Detection”)** + github: https://github.com/geometalab/OSMDeepOD **Selfie Detection by Synergy-Constraint Based Convolutional Neural Network** + intro: IEEE SITIS 2016 + arxiv: https://arxiv.org/abs/1611.04357 **Associative Embedding:End-to-End Learning for Joint Detection and Grouping** + arxiv: https://arxiv.org/abs/1611.05424 **Deep Cuboid Detection: Beyond 2D Bounding Boxes** + intro: CMU & Magic Leap + arxiv: https://arxiv.org/abs/1611.10010 **Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection** + arxiv: https://arxiv.org/abs/1612.03019 **Deep Learning Logo Detection with Data Expansion by Synthesising Context** + arxiv: https://arxiv.org/abs/1612.09322 **Scalable Deep Learning Logo Detection** + https://arxiv.org/abs/1803.11417 **Pixel-wise Ear Detection with Convolutional Encoder-Decoder Networks** + arxiv: https://arxiv.org/abs/1702.00307 **Automatic Handgun Detection Alarm in Videos Using Deep Learning** + arxiv: https://arxiv.org/abs/1702.05147 + results: https://github.com/SihamTabik/Pistol-Detection-in-Videos **Objects as context for part detection** + https://arxiv.org/abs/1703.09529 **Using Deep Networks for Drone Detection** + intro: AVSS 2017 + arxiv: https://arxiv.org/abs/1706.05726 **Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection** + intro: ICCV 2017 + arxiv: https://arxiv.org/abs/1708.01642 **Target Driven Instance Detection** + https://arxiv.org/abs/1803.04610 **DeepVoting: An Explainable Framework for Semantic Part Detection under Partial Occlusion** + https://arxiv.org/abs/1709.04577 **VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition** + intro: ICCV 2017 + arxiv: https://arxiv.org/abs/1710.06288 + github: https://github.com/SeokjuLee/VPGNet **Grab, Pay and Eat: Semantic Food Detection for Smart Restaurants** + https://arxiv.org/abs/1711.05128 **ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos** + intro: WACV 2018 + arxiv: https://arxiv.org/abs/1801.02031 **Deep Learning Object Detection Methods for Ecological Camera Trap Data** + intro: Conference of Computer and Robot Vision. University of Guelph + arxiv: https://arxiv.org/abs/1803.10842 **EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection** + https://arxiv.org/abs/1806.05525 **Towards End-to-End Lane Detection: an Instance Segmentation Approach** + arxiv: https://arxiv.org/abs/1802.05591 + github: https://github.com/MaybeShewill-CV/lanenet-lane-detection **iCAN: Instance-Centric Attention Network for Human-Object Interaction Detection** + intro: BMVC 2018 + project page: https://gaochen315.github.io/iCAN/ + arxiv: https://arxiv.org/abs/1808.10437 + github: https://github.com/vt-vl-lab/iCAN **Densely Supervised Grasp Detector (DSGD)** + https://arxiv.org/abs/1810.03962 **Object Proposal** **DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers** + arxiv: http://arxiv.org/abs/1510.04445 + github: https://github.com/aghodrati/deepproposal **Scale-aware Pixel-wise Object Proposal Networks** + intro: IEEE Transactions on Image Processing + arxiv: http://arxiv.org/abs/1601.04798 **Attend Refine Repeat: Active Box Proposal Generation via In-Out Localization** + intro: BMVC 2016. AttractioNet + arxiv: https://arxiv.org/abs/1606.04446 + github: https://github.com/gidariss/AttractioNet **Learning to Segment Object Proposals via Recursive Neural Networks** + arxiv: https://arxiv.org/abs/1612.01057 **Learning Detection with Diverse Proposals** + intro: CVPR 2017 **keywords: differentiable Determinantal Point Process (DPP) layer, Learning Detection with Diverse P roposals (LDDP)** + arxiv: https://arxiv.org/abs/1704.03533 **ScaleNet: Guiding Object Proposal Generation in Supermarkets and Beyond** + keywords: product detection + arxiv: https://arxiv.org/abs/1704.06752 **Improving Small Object Proposals for Company Logo Detection** + intro: ICMR 2017 + arxiv: https://arxiv.org/abs/1704.08881 **Open Logo Detection Challenge** + intro: BMVC 2018 + keywords: QMUL-OpenLogo + project page: https://qmul-openlogo.github.io/ + arxiv: https://arxiv.org/abs/1807.01964 **AttentionMask: Attentive, Efficient Object Proposal Generation Focusing on Small Objects** + intro: ACCV 2018 oral + arxiv: https://arxiv.org/abs/1811.08728 + github: https://github.com/chwilms/AttentionMask **Localization** **Beyond Bounding Boxes: Precise Localization of Objects in Images** + intro: PhD Thesis + homepage: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.html + phd-thesis: http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-193.pdf + github(“SDS using hypercolumns”): https://github.com/bharath272/sds **Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning** + arxiv: http://arxiv.org/abs/1503.00949 **Weakly Supervised Object Localization Using Size Estimates** + arxiv: http://arxiv.org/abs/1608.04314 **Active Object Localization with Deep Reinforcement Learning** + intro: ICCV 2015 + keywords: Markov Decision Process + arxiv: https://arxiv.org/abs/1511.06015 **Localizing objects using referring expressions** + intro: ECCV 2016 + keywords: LSTM, multiple instance learning (MIL) + paper: http://www.umiacs.umd.edu/~varun/files/refexp-ECCV16.pdf + github: https://github.com/varun-nagaraja/referring-expressions **LocNet: Improving Localization Accuracy for Object Detection** + intro: CVPR 2016 oral + arxiv: http://arxiv.org/abs/1511.07763 + github: https://github.com/gidariss/LocNet **Learning Deep Features for Discriminative Localization** + homepage: http://cnnlocalization.csail.mit.edu/ + arxiv: http://arxiv.org/abs/1512.04150 + github(Tensorflow): https://github.com/jazzsaxmafia/Weakly_detector + github: https://github.com/metalbubble/CAM + github: https://github.com/tdeboissiere/VGG16CAM-keras **ContextLocNet: Context-Aware Deep Network Models for Weakly Supervised Localization** + intro: ECCV 2016 + project page: http://www.di.ens.fr/willow/research/contextlocnet/ + arxiv: http://arxiv.org/abs/1609.04331 + github: https://github.com/vadimkantorov/contextlocnet **Ensemble of Part Detectors for Simultaneous Classification and Localization** + https://arxiv.org/abs/1705.10034 **STNet: Selective Tuning of Convolutional Networks for Object Localization** + https://arxiv.org/abs/1708.06418 **Soft Proposal Networks for Weakly Supervised Object Localization** + intro: ICCV 2017 + arxiv: https://arxiv.org/abs/1709.01829 **Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN** + intro: ACM MM 2017 + arxiv: https://arxiv.org/abs/1709.08295 **Tutorials / Talks** **Convolutional Feature Maps: Elements of efficient (and accurate) CNN-based object detection** + slides: http://research.microsoft.com/en-us/um/people/kahe/iccv15tutorial/iccv2015_tutorial_convolutional_feature_maps_kaiminghe.pdf **Towards Good Practices for Recognition & Detection** + intro: Hikvision Research Institute. Supervised Data Augmentation (SDA) + slides: http://image-net.org/challenges/talks/2016/Hikvision_at_ImageNet_2016.pdf **Work in progress: Improving object detection and instance segmentation for small objects** + https://docs.google.com/presentation/d/1OTfGn6mLe1VWE8D0q6Tu_WwFTSoLGd4OF8WCYnOWcVo/edit#slide=id.g37418adc7a_0_229 **Object Detection with Deep Learning: A Review** + https://arxiv.org/abs/1807.05511 **Projects** **Detectron** + intro: FAIR’s research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet. + github: https://github.com/facebookresearch/Detectron **TensorBox: a simple framework for training neural networks to detect objects in images** + intro: “The basic model implements the simple and robust GoogLeNet-OverFeat algorithm. We additionally provide an implementation of the ReInspect algorithm” + github: https://github.com/Russell91/TensorBox **Object detection in torch: Implementation of some object detection frameworks in torch** + github: https://github.com/fmassa/object-detection.torch **Using DIGITS to train an Object Detection network** + github: https://github.com/NVIDIA/DIGITS/blob/master/examples/object-detection/README.md **FCN-MultiBox Detector** + intro: Full convolution MultiBox Detector (like SSD) implemented in Torch. + github: https://github.com/teaonly/FMD.torch **KittiBox: A car detection model implemented in Tensorflow.** + keywords: MultiNet + intro: KittiBox is a collection of scripts to train out model FastBox on the Kitti Object Detection Dataset + github: https://github.com/MarvinTeichmann/KittiBox **Deformable Convolutional Networks + MST + Soft-NMS** + github: https://github.com/bharatsingh430/Deformable-ConvNets **How to Build a Real-time Hand-Detector using Neural Networks (SSD) on Tensorflow** + blog: https://towardsdatascience.com/how-to-build-a-real-time-hand-detector-using-neural-networks-ssd-on-tensorflow-d6bac0e4b2ce + github: https://github.com//victordibia/handtracking **Metrics for object detection** + intro: Most popular metrics used to evaluate object detection algorithms + github: https://github.com/rafaelpadilla/Object-Detection-Metrics **MobileNetv2-SSDLite** + intro: Caffe implementation of SSD and SSDLite detection on MobileNetv2, converted from tensorflow. + github: https://github.com/chuanqi305/MobileNetv2-SSDLite **Leaderboard** **Detection Results: VOC2012** + intro: Competition “comp4” (train on additional data) + homepage: http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=4 Tools **BeaverDam: Video annotation tool for deep learning training labels** + https://github.com/antingshen/BeaverDam **Convolutional Neural Networks for Object Detection** + http://rnd.azoft.com/convolutional-neural-networks-object-detection/ **Introducing automatic object detection to visual search (Pinterest)** + keywords: Faster R-CNN + blog: https://engineering.pinterest.com/blog/introducing-automatic-object-detection-visual-search + demo: https://engineering.pinterest.com/sites/engineering/files/Visual%20Search%20V1%20-%20Video.mp4 + review: https://news.developer.nvidia.com/pinterest-introduces-the-future-of-visual-search/?mkt_tok=eyJpIjoiTnpaa01UWXpPRE0xTURFMiIsInQiOiJJRjcybjkwTmtmallORUhLOFFFODBDclFqUlB3SWlRVXJXb1MrQ013TDRIMGxLQWlBczFIeWg0TFRUdnN2UHY2ZWFiXC9QQVwvQzBHM3B0UzBZblpOSmUyU1FcLzNPWXI4cml2VERwTTJsOFwvOEk9In0%3D **Deep Learning for Object Detection with DIGITS** + blog: https://devblogs.nvidia.com/parallelforall/deep-learning-object-detection-digits/ **Analyzing The Papers Behind Facebook’s Computer Vision Approach** + keywords: DeepMask, SharpMask, MultiPathNet + blog: https://adeshpande3.github.io/adeshpande3.github.io/Analyzing-the-Papers-Behind-Facebook’s-Computer-Vision-Approach/ **Easily Create High Quality Object Detectors with Deep Learning** + intro: dlib v19.2 + blog: http://blog.dlib.net/2016/10/easily-create-high-quality-object.html **How to Train a Deep-Learned Object Detection Model in the Microsoft Cognitive Toolkit** + blog: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/ + github: https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN **Object Detection in Satellite Imagery, a Low Overhead Approach** + part 1: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-i-cbd96154a1b7#.2csh4iwx9 + part 2: https://medium.com/the-downlinq/object-detection-in-satellite-imagery-a-low-overhead-approach-part-ii-893f40122f92#.f9b7dgf64 **You Only Look Twice — Multi-Scale Object Detection in Satellite Imagery With Convolutional Neural Networks** + part 1: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-38dad1cf7571#.fmmi2o3of + part 2: https://medium.com/the-downlinq/you-only-look-twice-multi-scale-object-detection-in-satellite-imagery-with-convolutional-neural-34f72f659588#.nwzarsz1t **Faster R-CNN Pedestrian and Car Detection** + blog: https://bigsnarf.wordpress.com/2016/11/07/faster-r-cnn-pedestrian-and-car-detection/ + ipn: https://gist.github.com/bigsnarfdude/2f7b2144065f6056892a98495644d3e0#file-demo_faster_rcnn_notebook-ipynb + github: https://github.com/bigsnarfdude/Faster-RCNN_TF **Small U-Net for vehicle detection** + blog: https://medium.com/@vivek.yadav/small-u-net-for-vehicle-detection-9eec216f9fd6#.md4u80kad **Region of interest pooling explained** + blog: https://deepsense.io/region-of-interest-pooling-explained/ + github: https://github.com/deepsense-io/roi-pooling **Supercharge your Computer Vision models with the TensorFlow Object Detection API** + blog: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html + github: https://github.com/tensorflow/models/tree/master/object_detection **Understanding SSD MultiBox — Real-Time Object Detection In Deep Learning** + https://towardsdatascience.com/understanding-ssd-multibox-real-time-object-detection-in-deep-learning-495ef744fab **One-shot object detection** + http://machinethink.net/blog/object-detection/ **An overview of object detection: one-stage methods** + https://www.jeremyjordan.me/object-detection-one-stage/ **deep learning object detection** + intro: A paper list of object detection using deep learning. + arxiv: https://github.com/hoya012/deep_learning_object_detection