awesome-object-detection 今天药忘吃喽~ 2022-05-24 07:38 140阅读 0赞 # awesome-object-detection # This is a list of awesome articles about object detection. # Contents: # * R-CNN * Fast R-CNN * Faster R-CNN * Light-Head R-CNN * Cascade R-CNN * SPP-Net * YOLO * YOLOv2 * YOLOv3 * SSD * DSSD * FSSD * ESSD * MDSSD * Pelee * R-FCN * FPN * RetinaNet * MegDet * DetNet * ZSD Based on handong1587's github（[https://handong1587.github.io/deep\_learning/2015/10/09/object-detection.html）][https_handong1587.github.io_deep_learning_2015_10_09_object-detection.html] # Papers&Codes # ## R-CNN ## Rich feature hierarchies for accurate object detection and semantic segmentation * intro: R-CNN * arxiv: [http://arxiv.org/abs/1311.2524][http_arxiv.org_abs_1311.2524] * supp: [http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf][http_people.eecs.berkeley.edu_rbg_papers_r-cnn-cvpr-supp.pdf] * slides: [http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf][http_www.image-net.org_challenges_LSVRC_2013_slides_r-cnn-ilsvrc2013-workshop.pdf] * slides: [http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf][http_www.cs.berkeley.edu_rbg_slides_rcnn-cvpr14-slides.pdf] * github: [https://github.com/rbgirshick/rcnn][https_github.com_rbgirshick_rcnn] * notes: [http://zhangliliang.com/2014/07/23/paper-note-rcnn/][http_zhangliliang.com_2014_07_23_paper-note-rcnn] * caffe-pr("Make R-CNN the Caffe detection example"): [https://github.com/BVLC/caffe/pull/482][https_github.com_BVLC_caffe_pull_482] ## Fast R-CNN ## Fast R-CNN * arxiv: [http://arxiv.org/abs/1504.08083][http_arxiv.org_abs_1504.08083] * slides: [http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf][http_tutorial.caffe.berkeleyvision.org_caffe-cvpr15-detection.pdf] * github: [https://github.com/rbgirshick/fast-rcnn][https_github.com_rbgirshick_fast-rcnn] * github(COCO-branch): [https://github.com/rbgirshick/fast-rcnn/tree/coco][https_github.com_rbgirshick_fast-rcnn_tree_coco] * webcam demo: [https://github.com/rbgirshick/fast-rcnn/pull/29][https_github.com_rbgirshick_fast-rcnn_pull_29] * notes: [http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/][http_zhangliliang.com_2015_05_17_paper-note-fast-rcnn] * notes: [http://blog.csdn.net/linj\_m/article/details/48930179][http_blog.csdn.net_linj_m_article_details_48930179] * github("Fast R-CNN in MXNet"): [https://github.com/precedenceguo/mx-rcnn][https_github.com_precedenceguo_mx-rcnn] * github: [https://github.com/mahyarnajibi/fast-rcnn-torch][https_github.com_mahyarnajibi_fast-rcnn-torch] * github: [https://github.com/apple2373/chainer-simple-fast-rnn][https_github.com_apple2373_chainer-simple-fast-rnn] * github: [https://github.com/zplizzi/tensorflow-fast-rcnn][https_github.com_zplizzi_tensorflow-fast-rcnn] A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection * intro: CVPR 2017 * arxiv: [https://arxiv.org/abs/1704.03414][https_arxiv.org_abs_1704.03414] * paper: [http://abhinavsh.info/papers/pdfs/adversarial\_object\_detection.pdf][http_abhinavsh.info_papers_pdfs_adversarial_object_detection.pdf] * github(Caffe): [https://github.com/xiaolonw/adversarial-frcnn][https_github.com_xiaolonw_adversarial-frcnn] ## Faster R-CNN ## Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks * intro: NIPS 2015 * arxiv: [http://arxiv.org/abs/1506.01497][http_arxiv.org_abs_1506.01497] * gitxiv: [http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region][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][http_web.cs.hacettepe.edu.tr_aykut_classes_spring2016_bil722_slides_w05-FasterR-CNN.pdf] * github(official, Matlab): [https://github.com/ShaoqingRen/faster\_rcnn][https_github.com_ShaoqingRen_faster_rcnn] * github(Caffe): [https://github.com/rbgirshick/py-faster-rcnn][https_github.com_rbgirshick_py-faster-rcnn] * github(MXNet): [https://github.com/msracver/Deformable-ConvNets/tree/master/faster\_rcnn][https_github.com_msracver_Deformable-ConvNets_tree_master_faster_rcnn] * github(PyTorch--recommend): [https://github.com//jwyang/faster-rcnn.pytorch][https_github.com_jwyang_faster-rcnn.pytorch] * github: [https://github.com/mitmul/chainer-faster-rcnn][https_github.com_mitmul_chainer-faster-rcnn] * github(Torch):: [https://github.com/andreaskoepf/faster-rcnn.torch][https_github.com_andreaskoepf_faster-rcnn.torch] * github(Torch):: [https://github.com/ruotianluo/Faster-RCNN-Densecap-torch][https_github.com_ruotianluo_Faster-RCNN-Densecap-torch] * github(TensorFlow): [https://github.com/smallcorgi/Faster-RCNN\_TF][https_github.com_smallcorgi_Faster-RCNN_TF] * github(TensorFlow): [https://github.com/CharlesShang/TFFRCNN][https_github.com_CharlesShang_TFFRCNN] * github(C++ demo): [https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus][https_github.com_YihangLou_FasterRCNN-Encapsulation-Cplusplus] * github(Keras): [https://github.com/yhenon/keras-frcnn][https_github.com_yhenon_keras-frcnn] * github: [https://github.com/Eniac-Xie/faster-rcnn-resnet][https_github.com_Eniac-Xie_faster-rcnn-resnet] * github(C++): [https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev][https_github.com_D-X-Y_caffe-faster-rcnn_tree_dev] R-CNN minus R * intro: BMVC 2015 * arxiv: [http://arxiv.org/abs/1506.06981][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][https_github.com_dmlc_mxnet_tree_master_example_rcnn] Contextual Priming and Feedback for Faster R-CNN * intro: ECCV 2016. Carnegie Mellon University * paper: [http://abhinavsh.info/context\_priming\_feedback.pdf][http_abhinavsh.info_context_priming_feedback.pdf] * poster: [http://www.eccv2016.org/files/posters/P-1A-20.pdf][http_www.eccv2016.org_files_posters_P-1A-20.pdf] An Implementation of Faster RCNN with Study for Region Sampling * intro: Technical Report, 3 pages. CMU * arxiv: [https://arxiv.org/abs/1702.02138][https_arxiv.org_abs_1702.02138] * github: [https://github.com/endernewton/tf-faster-rcnn][https_github.com_endernewton_tf-faster-rcnn] Interpretable R-CNN * intro: North Carolina State University & Alibaba * keywords: AND-OR Graph (AOG) * arxiv: [https://arxiv.org/abs/1711.05226][https_arxiv.org_abs_1711.05226] ## Light-Head R-CNN ## Light-Head R-CNN: In Defense of Two-Stage Object Detector * intro: Tsinghua University & Megvii Inc * arxiv: [https://arxiv.org/abs/1711.07264][https_arxiv.org_abs_1711.07264] * github(offical): [https://github.com/zengarden/light\_head\_rcnn][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][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][https_arxiv.org_abs_1712.00726] * github: [https://github.com/zhaoweicai/cascade-rcnn][https_github.com_zhaoweicai_cascade-rcnn] ## SPP-Net ## Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition * intro: ECCV 2014 / TPAMI 2015 * arxiv: [http://arxiv.org/abs/1406.4729][http_arxiv.org_abs_1406.4729] * github: [https://github.com/ShaoqingRen/SPP\_net][https_github.com_ShaoqingRen_SPP_net] * notes: [http://zhangliliang.com/2014/09/13/paper-note-sppnet/][http_zhangliliang.com_2014_09_13_paper-note-sppnet] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection * intro: PAMI 2016 * 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][http_www.ee.cuhk.edu.hk_CB_9Cwlouyang_projects_imagenetDeepId_index.html] * arxiv: [http://arxiv.org/abs/1412.5661][http_arxiv.org_abs_1412.5661] Object Detectors Emerge in Deep Scene CNNs * intro: ICLR 2015 * arxiv: [http://arxiv.org/abs/1412.6856][http_arxiv.org_abs_1412.6856] * paper: [https://www.robots.ox.ac.uk/~vgg/rg/papers/zhou\_iclr15.pdf][https_www.robots.ox.ac.uk_vgg_rg_papers_zhou_iclr15.pdf] * paper: [https://people.csail.mit.edu/khosla/papers/iclr2015\_zhou.pdf][https_people.csail.mit.edu_khosla_papers_iclr2015_zhou.pdf] * slides: [http://places.csail.mit.edu/slide\_iclr2015.pdf][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][https_www.cs.toronto.edu_yukun_segdeepm.html] * arxiv: [https://arxiv.org/abs/1502.04275][https_arxiv.org_abs_1502.04275] * github: [https://github.com/YknZhu/segDeepM][https_github.com_YknZhu_segDeepM] Object Detection Networks on Convolutional Feature Maps * intro: TPAMI 2015 * keywords: NoC * arxiv: [http://arxiv.org/abs/1504.06066][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][http_arxiv.org_abs_1504.03293] * slides: [http://www.ytzhang.net/files/publications/2015-cvpr-det-slides.pdf][http_www.ytzhang.net_files_publications_2015-cvpr-det-slides.pdf] * github: [https://github.com/YutingZhang/fgs-obj][https_github.com_YutingZhang_fgs-obj] DeepBox: Learning Objectness with Convolutional Networks * keywords: DeepBox * arxiv: [http://arxiv.org/abs/1505.02146][http_arxiv.org_abs_1505.02146] * github: [https://github.com/weichengkuo/DeepBox][https_github.com_weichengkuo_DeepBox] ## YOLO ## You Only Look Once: Unified, Real-Time Object Detection [![img][]][img] * arxiv: [http://arxiv.org/abs/1506.02640][http_arxiv.org_abs_1506.02640] * code: [https://pjreddie.com/darknet/yolov1/][https_pjreddie.com_darknet_yolov1] * github: [https://github.com/pjreddie/darknet][https_github.com_pjreddie_darknet] * blog: [https://pjreddie.com/darknet/yolov1/][https_pjreddie.com_darknet_yolov1] * slides: [https://docs.google.com/presentation/d/1aeRvtKG21KHdD5lg6Hgyhx5rPq\_ZOsGjG5rJ1HP7BbA/pub?start=false&loop=false&delayms=3000&slide=id.p][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/][https_www.reddit.com_r_MachineLearning_comments_3a3m0o_realtime_object_detection_with_yolo] * github: [https://github.com/gliese581gg/YOLO\_tensorflow][https_github.com_gliese581gg_YOLO_tensorflow] * github: [https://github.com/xingwangsfu/caffe-yolo][https_github.com_xingwangsfu_caffe-yolo] * github: [https://github.com/frankzhangrui/Darknet-Yolo][https_github.com_frankzhangrui_Darknet-Yolo] * github: [https://github.com/BriSkyHekun/py-darknet-yolo][https_github.com_BriSkyHekun_py-darknet-yolo] * github: [https://github.com/tommy-qichang/yolo.torch][https_github.com_tommy-qichang_yolo.torch] * github: [https://github.com/frischzenger/yolo-windows][https_github.com_frischzenger_yolo-windows] * github: [https://github.com/AlexeyAB/yolo-windows][https_github.com_AlexeyAB_yolo-windows] * github: [https://github.com/nilboy/tensorflow-yolo][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][https_thtrieu.github.io_notes_yolo-tensorflow-graph-buffer-cpp] * github: [https://github.com/thtrieu/darkflow][https_github.com_thtrieu_darkflow] Start Training YOLO with Our Own Data [![img][img 1]][img 1] * intro: train with customized data and class numbers/labels. Linux / Windows version for darknet. * blog: [http://guanghan.info/blog/en/my-works/train-yolo/][http_guanghan.info_blog_en_my-works_train-yolo] * github: [https://github.com/Guanghan/darknet][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/][http_machinethink.net_blog_yolo-coreml-versus-mps-graph] * github: [https://github.com/hollance/YOLO-CoreML-MPSNNGraph][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][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/][https_sriraghu.com_2017_07_12_computer-vision-in-ios-object-detection] * github:[https://github.com/r4ghu/iOS-CoreML-Yolo][https_github.com_r4ghu_iOS-CoreML-Yolo] ## YOLOv2 ## YOLO9000: Better, Faster, Stronger * arxiv: [https://arxiv.org/abs/1612.08242][https_arxiv.org_abs_1612.08242] * code: [http://pjreddie.com/yolo9000/][http_pjreddie.com_yolo9000] [https://pjreddie.com/darknet/yolov2/][https_pjreddie.com_darknet_yolov2] * github(Chainer): [https://github.com/leetenki/YOLOv2][https_github.com_leetenki_YOLOv2] * github(Keras): [https://github.com/allanzelener/YAD2K][https_github.com_allanzelener_YAD2K] * github(PyTorch): [https://github.com/longcw/yolo2-pytorch][https_github.com_longcw_yolo2-pytorch] * github(Tensorflow): [https://github.com/hizhangp/yolo\_tensorflow][https_github.com_hizhangp_yolo_tensorflow] * github(Windows): [https://github.com/AlexeyAB/darknet][https_github.com_AlexeyAB_darknet] * github: [https://github.com/choasUp/caffe-yolo9000][https_github.com_choasUp_caffe-yolo9000] * github: [https://github.com/philipperemy/yolo-9000][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][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][https_github.com_AlexeyAB_Yolo_mark] LightNet: Bringing pjreddie's DarkNet out of the shadows [https://github.com//explosion/lightnet][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][https_github.com_Cartucho_yolo-boundingbox-labeler-GUI] Loss Rank Mining: A General Hard Example Mining Method for Real-time Detectors * intro: LRM is the first hard example mining strategy which could fit YOLOv2 perfectly and make it better applied in series of real scenarios where both real-time rates and accurate detection are strongly demanded. * arxiv: [https://arxiv.org/abs/1804.04606][https_arxiv.org_abs_1804.04606] Object detection at 200 Frames Per Second * intro: faster than Tiny-Yolo-v2 * arXiv: [https://arxiv.org/abs/1805.06361][https_arxiv.org_abs_1805.06361] ## YOLOv3 ## YOLOv3: An Incremental Improvement * arxiv:[https://arxiv.org/abs/1804.02767][https_arxiv.org_abs_1804.02767] * paper:[https://pjreddie.com/media/files/papers/YOLOv3.pdf][https_pjreddie.com_media_files_papers_YOLOv3.pdf] * code: [https://pjreddie.com/darknet/yolo/][https_pjreddie.com_darknet_yolo] * github(Official):[https://github.com/pjreddie/darknet][https_github.com_pjreddie_darknet] * github:[https://github.com/experiencor/keras-yolo3][https_github.com_experiencor_keras-yolo3] * github:[https://github.com/qqwweee/keras-yolo3][https_github.com_qqwweee_keras-yolo3] * github:[https://github.com/marvis/pytorch-yolo3][https_github.com_marvis_pytorch-yolo3] * github:[https://github.com/ayooshkathuria/pytorch-yolo-v3][https_github.com_ayooshkathuria_pytorch-yolo-v3] * github:[https://github.com/ayooshkathuria/YOLO\_v3\_tutorial\_from\_scratch][https_github.com_ayooshkathuria_YOLO_v3_tutorial_from_scratch] ## SSD ## SSD: Single Shot MultiBox Detector [![img][img 2]][img 2] * intro: ECCV 2016 Oral * arxiv: [http://arxiv.org/abs/1512.02325][http_arxiv.org_abs_1512.02325] * paper: [http://www.cs.unc.edu/~wliu/papers/ssd.pdf][http_www.cs.unc.edu_wliu_papers_ssd.pdf] * slides: [http://www.cs.unc.edu/%7Ewliu/papers/ssd\_eccv2016\_slide.pdf][http_www.cs.unc.edu_7Ewliu_papers_ssd_eccv2016_slide.pdf] * github(Official): [https://github.com/weiliu89/caffe/tree/ssd][https_github.com_weiliu89_caffe_tree_ssd] * video: [http://weibo.com/p/2304447a2326da963254c963c97fb05dd3a973][http_weibo.com_p_2304447a2326da963254c963c97fb05dd3a973] * github: [https://github.com/zhreshold/mxnet-ssd][https_github.com_zhreshold_mxnet-ssd] * github: [https://github.com/zhreshold/mxnet-ssd.cpp][https_github.com_zhreshold_mxnet-ssd.cpp] * github: [https://github.com/rykov8/ssd\_keras][https_github.com_rykov8_ssd_keras] * github: [https://github.com/balancap/SSD-Tensorflow][https_github.com_balancap_SSD-Tensorflow] * github: [https://github.com/amdegroot/ssd.pytorch][https_github.com_amdegroot_ssd.pytorch] * github(Caffe): [https://github.com/chuanqi305/MobileNet-SSD][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][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][https_arxiv.org_abs_1701.06659] * github: [https://github.com/chengyangfu/caffe/tree/dssd][https_github.com_chengyangfu_caffe_tree_dssd] * github: [https://github.com/MTCloudVision/mxnet-dssd][https_github.com_MTCloudVision_mxnet-dssd] * demo: [http://120.52.72.53/www.cs.unc.edu/c3pr90ntc0td/~cyfu/dssd\_lalaland.mp4][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][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][https_arxiv.org_abs_1707.08682] Feature-Fused SSD: Fast Detection for Small Objects [https://arxiv.org/abs/1709.05054][https_arxiv.org_abs_1709.05054] ## FSSD ## FSSD: Feature Fusion Single Shot Multibox Detector [https://arxiv.org/abs/1712.00960][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][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][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][https_arxiv.org_abs_1802.06488] ## MDSSD ## MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects * arxiv: [https://arxiv.org/abs/1805.07009][https_arxiv.org_abs_1805.07009] ## Pelee ## Pelee: A Real-Time Object Detection System on Mobile Devices [https://github.com/Robert-JunWang/Pelee][https_github.com_Robert-JunWang_Pelee] * intro: (ICLR 2018 workshop track) * arxiv: [https://arxiv.org/abs/1804.06882][https_arxiv.org_abs_1804.06882] * github: [https://github.com/Robert-JunWang/Pelee][https_github.com_Robert-JunWang_Pelee] ## R-FCN ## R-FCN: Object Detection via Region-based Fully Convolutional Networks * arxiv: [http://arxiv.org/abs/1605.06409][http_arxiv.org_abs_1605.06409] * github: [https://github.com/daijifeng001/R-FCN][https_github.com_daijifeng001_R-FCN] * github(MXNet): [https://github.com/msracver/Deformable-ConvNets/tree/master/rfcn][https_github.com_msracver_Deformable-ConvNets_tree_master_rfcn] * github: [https://github.com/Orpine/py-R-FCN][https_github.com_Orpine_py-R-FCN] * github: [https://github.com/PureDiors/pytorch\_RFCN][https_github.com_PureDiors_pytorch_RFCN] * github: [https://github.com/bharatsingh430/py-R-FCN-multiGPU][https_github.com_bharatsingh430_py-R-FCN-multiGPU] * github: [https://github.com/xdever/RFCN-tensorflow][https_github.com_xdever_RFCN-tensorflow] R-FCN-3000 at 30fps: Decoupling Detection and Classification [https://arxiv.org/abs/1712.01802][https_arxiv.org_abs_1712.01802] Recycle deep features for better object detection * arxiv: [http://arxiv.org/abs/1607.05066][http_arxiv.org_abs_1607.05066] ## FPN ## Feature Pyramid Networks for Object Detection * intro: Facebook AI Research * arxiv: [https://arxiv.org/abs/1612.03144][https_arxiv.org_abs_1612.03144] Action-Driven Object Detection with Top-Down Visual Attentions * arxiv: [https://arxiv.org/abs/1612.06704][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][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][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][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][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][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][https_arxiv.org_abs_1704.03944] Spatial Memory for Context Reasoning in Object Detection * arxiv: [https://arxiv.org/abs/1704.04224][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][https_arxiv.org_abs_1704.05776] * github: [https://github.com/xiaohaoChen/rrc\_detection][https_github.com_xiaohaoChen_rrc_detection] Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection [https://arxiv.org/abs/1704.05775][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][https_arxiv.org_abs_1705.05922] Point Linking Network for Object Detection * intro: Point Linking Network (PLN) * arxiv: [https://arxiv.org/abs/1706.03646][https_arxiv.org_abs_1706.03646] Perceptual Generative Adversarial Networks for Small Object Detection [https://arxiv.org/abs/1706.05274][https_arxiv.org_abs_1706.05274] Few-shot Object Detection [https://arxiv.org/abs/1706.08249][https_arxiv.org_abs_1706.08249] Yes-Net: An effective Detector Based on Global Information [https://arxiv.org/abs/1706.09180][https_arxiv.org_abs_1706.09180] SMC Faster R-CNN: Toward a scene-specialized multi-object detector [https://arxiv.org/abs/1706.10217][https_arxiv.org_abs_1706.10217] Towards lightweight convolutional neural networks for object detection [https://arxiv.org/abs/1707.01395][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][https_arxiv.org_abs_1707.01691] * github: [https://github.com/taokong/RON][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][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][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][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][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][https_arxiv.org_abs_1707.09531] * github: [https://github.com/sciencefans/RSA-for-object-detection][https_github.com_sciencefans_RSA-for-object-detection] ## DSOD ## DSOD: Learning Deeply Supervised Object Detectors from Scratch [![img][img 3]][img 3] * intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China * arxiv: [https://arxiv.org/abs/1708.01241][https_arxiv.org_abs_1708.01241] * github: [https://github.com/szq0214/DSOD][https_github.com_szq0214_DSOD] * github:[https://github.com/Windaway/DSOD-Tensorflow][https_github.com_Windaway_DSOD-Tensorflow] * github:[https://github.com/chenyuntc/dsod.pytorch][https_github.com_chenyuntc_dsod.pytorch] Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids * arxiv:[https://arxiv.org/abs/1712.00886][https_arxiv.org_abs_1712.00886] * github:[https://github.com/szq0214/GRP-DSOD][https_github.com_szq0214_GRP-DSOD] ## 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][https_arxiv.org_abs_1708.02002] CoupleNet: Coupling Global Structure with Local Parts for Object Detection * intro: ICCV 2017 * arxiv: [https://arxiv.org/abs/1708.02863][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][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][https_arxiv.org_abs_1709.04347] StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection [https://arxiv.org/abs/1709.05788][https_arxiv.org_abs_1709.05788] Dynamic Zoom-in Network for Fast Object Detection in Large Images [https://arxiv.org/abs/1711.05187][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][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][https_arxiv.org_abs_1711.07240] Single-Shot Refinement Neural Network for Object Detection * arxiv: [https://arxiv.org/abs/1711.06897][https_arxiv.org_abs_1711.06897] * github: [https://github.com/sfzhang15/RefineDet][https_github.com_sfzhang15_RefineDet] Receptive Field Block Net for Accurate and Fast Object Detection * intro: RFBNet * arxiv: [https://arxiv.org/abs/1711.07767][https_arxiv.org_abs_1711.07767] * github: [https://github.com//ruinmessi/RFBNet][https_github.com_ruinmessi_RFBNet] An Analysis of Scale Invariance in Object Detection - SNIP * arxiv: [https://arxiv.org/abs/1711.08189][https_arxiv.org_abs_1711.08189] * github: [https://github.com/bharatsingh430/snip][https_github.com_bharatsingh430_snip] Feature Selective Networks for Object Detection [https://arxiv.org/abs/1711.08879][https_arxiv.org_abs_1711.08879] Learning a Rotation Invariant Detector with Rotatable Bounding Box * arxiv: [https://arxiv.org/abs/1711.09405][https_arxiv.org_abs_1711.09405] * github: [https://github.com/liulei01/DRBox][https_github.com_liulei01_DRBox] Scalable Object Detection for Stylized Objects * intro: Microsoft AI & Research Munich * arxiv: [https://arxiv.org/abs/1711.09822][https_arxiv.org_abs_1711.09822] Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids * arxiv: [https://arxiv.org/abs/1712.00886][https_arxiv.org_abs_1712.00886] * github: [https://github.com/szq0214/GRP-DSOD][https_github.com_szq0214_GRP-DSOD] Deep Regionlets for Object Detection * keywords: region selection network, gating network * arxiv: [https://arxiv.org/abs/1712.02408][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][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][https_arxiv.org_abs_1712.08832] Spot the Difference by Object Detection * intro: Tsinghua University & JD Group * arxiv: [https://arxiv.org/abs/1801.01051][https_arxiv.org_abs_1801.01051] Localization-Aware Active Learning for Object Detection * arxiv: [https://arxiv.org/abs/1801.05124][https_arxiv.org_abs_1801.05124] Object Detection with Mask-based Feature Encoding [https://arxiv.org/abs/1802.03934][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][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][https_arxiv.org_abs_1803.03243] Pseudo Mask Augmented Object Detection [https://arxiv.org/abs/1803.05858][https_arxiv.org_abs_1803.05858] Revisiting RCNN: On Awakening the Classification Power of Faster RCNN [https://arxiv.org/abs/1803.06799][https_arxiv.org_abs_1803.06799] Zero-Shot Detection * intro: Australian National University * keywords: YOLO * arxiv: [https://arxiv.org/abs/1803.07113][https_arxiv.org_abs_1803.07113] Learning Region Features for Object Detection * intro: Peking University & MSRA * arxiv: [https://arxiv.org/abs/1803.07066][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][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][https_arxiv.org_abs_1803.08670] Task-Driven Super Resolution: Object Detection in Low-resolution Images [https://arxiv.org/abs/1803.11316][https_arxiv.org_abs_1803.11316] Transferring Common-Sense Knowledge for Object Detection [https://arxiv.org/abs/1804.01077][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][https_arxiv.org_abs_1804.00428] * github: [https://github.com/Hwang64/MLKP][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][https_arxiv.org_abs_1804.04606] Robust Physical Adversarial Attack on Faster R-CNN Object Detector [https://arxiv.org/abs/1804.05810][https_arxiv.org_abs_1804.05810] ## DetNet ## DetNet: A Backbone network for Object Detection * intro: Tsinghua University & Face++ * arxiv: [https://arxiv.org/abs/1804.06215][https_arxiv.org_abs_1804.06215] ## 3D Object Detection ## LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs * arxiv: [https://arxiv.org/abs/1805.04902][https_arxiv.org_abs_1805.04902] * github: [https://github.com/CPFL/Autoware/tree/feature/cnn\_lidar\_detection][https_github.com_CPFL_Autoware_tree_feature_cnn_lidar_detection] ## ZSD ## Zero-Shot Object Detection * arxiv: [https://arxiv.org/abs/1804.04340][https_arxiv.org_abs_1804.04340] Zero-Shot Object Detection: Learning to Simultaneously Recognize and Localize Novel Concepts * arxiv: [https://arxiv.org/abs/1803.06049][https_arxiv.org_abs_1803.06049] Zero-Shot Object Detection by Hybrid Region Embedding * arxiv: [https://arxiv.org/abs/1805.06157][https_arxiv.org_abs_1805.06157] ## Other ## Relation Network for Object Detection * intro: CVPR 2018 * arxiv: [https://arxiv.org/abs/1711.11575][https_arxiv.org_abs_1711.11575] Quantization Mimic: Towards Very Tiny CNN for Object Detection * Tsinghua University1 & The Chinese University of Hong Kong2 &SenseTime3 * arxiv: [https://arxiv.org/abs/1805.02152][https_arxiv.org_abs_1805.02152] Learning Rich Features for Image Manipulation Detection * intro: CVPR 2018 Camera Ready * arxiv: [https://arxiv.org/abs/1805.04953][https_arxiv.org_abs_1805.04953] [https_handong1587.github.io_deep_learning_2015_10_09_object-detection.html]: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html%EF%BC%89 [http_arxiv.org_abs_1311.2524]: http://arxiv.org/abs/1311.2524 [http_people.eecs.berkeley.edu_rbg_papers_r-cnn-cvpr-supp.pdf]: http://people.eecs.berkeley.edu/~rbg/papers/r-cnn-cvpr-supp.pdf [http_www.image-net.org_challenges_LSVRC_2013_slides_r-cnn-ilsvrc2013-workshop.pdf]: http://www.image-net.org/challenges/LSVRC/2013/slides/r-cnn-ilsvrc2013-workshop.pdf [http_www.cs.berkeley.edu_rbg_slides_rcnn-cvpr14-slides.pdf]: http://www.cs.berkeley.edu/~rbg/slides/rcnn-cvpr14-slides.pdf [https_github.com_rbgirshick_rcnn]: https://github.com/rbgirshick/rcnn [http_zhangliliang.com_2014_07_23_paper-note-rcnn]: http://zhangliliang.com/2014/07/23/paper-note-rcnn/ [https_github.com_BVLC_caffe_pull_482]: https://github.com/BVLC/caffe/pull/482 [http_arxiv.org_abs_1504.08083]: http://arxiv.org/abs/1504.08083 [http_tutorial.caffe.berkeleyvision.org_caffe-cvpr15-detection.pdf]: http://tutorial.caffe.berkeleyvision.org/caffe-cvpr15-detection.pdf [https_github.com_rbgirshick_fast-rcnn]: https://github.com/rbgirshick/fast-rcnn [https_github.com_rbgirshick_fast-rcnn_tree_coco]: https://github.com/rbgirshick/fast-rcnn/tree/coco [https_github.com_rbgirshick_fast-rcnn_pull_29]: https://github.com/rbgirshick/fast-rcnn/pull/29 [http_zhangliliang.com_2015_05_17_paper-note-fast-rcnn]: http://zhangliliang.com/2015/05/17/paper-note-fast-rcnn/ [http_blog.csdn.net_linj_m_article_details_48930179]: http://blog.csdn.net/linj_m/article/details/48930179 [https_github.com_precedenceguo_mx-rcnn]: https://github.com/precedenceguo/mx-rcnn [https_github.com_mahyarnajibi_fast-rcnn-torch]: https://github.com/mahyarnajibi/fast-rcnn-torch [https_github.com_apple2373_chainer-simple-fast-rnn]: https://github.com/apple2373/chainer-simple-fast-rnn [https_github.com_zplizzi_tensorflow-fast-rcnn]: https://github.com/zplizzi/tensorflow-fast-rcnn [https_arxiv.org_abs_1704.03414]: https://arxiv.org/abs/1704.03414 [http_abhinavsh.info_papers_pdfs_adversarial_object_detection.pdf]: http://abhinavsh.info/papers/pdfs/adversarial_object_detection.pdf [https_github.com_xiaolonw_adversarial-frcnn]: https://github.com/xiaolonw/adversarial-frcnn [http_arxiv.org_abs_1506.01497]: http://arxiv.org/abs/1506.01497 [http_www.gitxiv.com_posts_8pfpcvefDYn2gSgXk_faster-r-cnn-towards-real-time-object-detection-with-region]: http://www.gitxiv.com/posts/8pfpcvefDYn2gSgXk/faster-r-cnn-towards-real-time-object-detection-with-region [http_web.cs.hacettepe.edu.tr_aykut_classes_spring2016_bil722_slides_w05-FasterR-CNN.pdf]: http://web.cs.hacettepe.edu.tr/~aykut/classes/spring2016/bil722/slides/w05-FasterR-CNN.pdf [https_github.com_ShaoqingRen_faster_rcnn]: https://github.com/ShaoqingRen/faster_rcnn [https_github.com_rbgirshick_py-faster-rcnn]: https://github.com/rbgirshick/py-faster-rcnn [https_github.com_msracver_Deformable-ConvNets_tree_master_faster_rcnn]: https://github.com/msracver/Deformable-ConvNets/tree/master/faster_rcnn [https_github.com_jwyang_faster-rcnn.pytorch]: https://github.com//jwyang/faster-rcnn.pytorch [https_github.com_mitmul_chainer-faster-rcnn]: https://github.com/mitmul/chainer-faster-rcnn [https_github.com_andreaskoepf_faster-rcnn.torch]: https://github.com/andreaskoepf/faster-rcnn.torch [https_github.com_ruotianluo_Faster-RCNN-Densecap-torch]: https://github.com/ruotianluo/Faster-RCNN-Densecap-torch [https_github.com_smallcorgi_Faster-RCNN_TF]: https://github.com/smallcorgi/Faster-RCNN_TF [https_github.com_CharlesShang_TFFRCNN]: https://github.com/CharlesShang/TFFRCNN [https_github.com_YihangLou_FasterRCNN-Encapsulation-Cplusplus]: https://github.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus [https_github.com_yhenon_keras-frcnn]: https://github.com/yhenon/keras-frcnn [https_github.com_Eniac-Xie_faster-rcnn-resnet]: https://github.com/Eniac-Xie/faster-rcnn-resnet [https_github.com_D-X-Y_caffe-faster-rcnn_tree_dev]: https://github.com/D-X-Y/caffe-faster-rcnn/tree/dev [http_arxiv.org_abs_1506.06981]: http://arxiv.org/abs/1506.06981 [https_github.com_dmlc_mxnet_tree_master_example_rcnn]: https://github.com/dmlc/mxnet/tree/master/example/rcnn [http_abhinavsh.info_context_priming_feedback.pdf]: http://abhinavsh.info/context_priming_feedback.pdf [http_www.eccv2016.org_files_posters_P-1A-20.pdf]: http://www.eccv2016.org/files/posters/P-1A-20.pdf [https_arxiv.org_abs_1702.02138]: https://arxiv.org/abs/1702.02138 [https_github.com_endernewton_tf-faster-rcnn]: https://github.com/endernewton/tf-faster-rcnn [https_arxiv.org_abs_1711.05226]: https://arxiv.org/abs/1711.05226 [https_arxiv.org_abs_1711.07264]: https://arxiv.org/abs/1711.07264 [https_github.com_zengarden_light_head_rcnn]: https://github.com/zengarden/light_head_rcnn [https_github.com_terrychenism_Deformable-ConvNets_blob_master_rfcn_symbols_resnet_v1_101_rfcn_light.py_L784]: https://github.com/terrychenism/Deformable-ConvNets/blob/master/rfcn/symbols/resnet_v1_101_rfcn_light.py#L784 [https_arxiv.org_abs_1712.00726]: https://arxiv.org/abs/1712.00726 [https_github.com_zhaoweicai_cascade-rcnn]: https://github.com/zhaoweicai/cascade-rcnn [http_arxiv.org_abs_1406.4729]: http://arxiv.org/abs/1406.4729 [https_github.com_ShaoqingRen_SPP_net]: https://github.com/ShaoqingRen/SPP_net [http_zhangliliang.com_2014_09_13_paper-note-sppnet]: http://zhangliliang.com/2014/09/13/paper-note-sppnet/ [http_www.ee.cuhk.edu.hk_CB_9Cwlouyang_projects_imagenetDeepId_index.html]: 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