# awesome-object-detection **Repository Path**: python-deathfans/awesome-object-detection ## Basic Information - **Project Name**: awesome-object-detection - **Description**: Awesome Object Detection based on handong1587 github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-06-25 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # object-detection [TOC] This is a list of awesome articles about object detection. If you want to read the paper according to time, you can refer to [Date](Date.md). - R-CNN - Fast R-CNN - Faster R-CNN - Mask R-CNN - Light-Head R-CNN - Cascade R-CNN - SPP-Net - YOLO - YOLOv2 - YOLOv3 - YOLT - SSD - DSSD - FSSD - ESSD - MDSSD - Pelee - Fire SSD - R-FCN - FPN - DSOD - RetinaNet - MegDet - RefineNet - DetNet - SSOD - CornerNet - M2Det - 3D Object Detection - ZSD(Zero-Shot Object Detection) - OSD(One-Shot object Detection) - Weakly Supervised Object Detection - Softer-NMS - 2018 - 2019 - Other Based on handong1587's github: https://handong1587.github.io/deep_learning/2015/10/09/object-detection.html # Survey **Imbalance Problems in Object Detection: A Review** - intro: under review at TPAMI - arXiv: **Recent Advances in Deep Learning for Object Detection** - intro: From 2013 (OverFeat) to 2019 (DetNAS) - arXiv: **A Survey of Deep Learning-based Object Detection** - intro:From Fast R-CNN to NAS-FPN - arXiv: **Object Detection in 20 Years: A Survey** - intro:This work has been submitted to the IEEE TPAMI for possible publication - arXiv: **《Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks》** - intro: awesome - arXiv: https://arxiv.org/abs/1809.03193 **《Deep Learning for Generic Object Detection: A Survey》** - intro: Submitted to IJCV 2018 - arXiv: https://arxiv.org/abs/1809.02165 # Papers&Codes ## R-CNN **Rich feature hierarchies for accurate object detection and semantic segmentation** - intro: R-CNN - arxiv: - supp: - slides: - slides: - github: - notes: - caffe-pr("Make R-CNN the Caffe detection example"): ## Fast R-CNN **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** - intro: CVPR 2017 - arxiv: - paper: - github(Caffe): ## Faster R-CNN **Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks** - intro: NIPS 2015 - arxiv: - gitxiv: - slides: - github(official, Matlab): - github(Caffe): - github(MXNet): - github(PyTorch--recommend): - github: - github(Torch):: - github(Torch):: - github(TensorFlow): - github(TensorFlow): - github(C++ demo): - github(Keras): - github: - github(C++): **R-CNN minus R** - intro: BMVC 2015 - arxiv: **Faster R-CNN in MXNet with distributed implementation and data parallelization** - github: **Contextual Priming and Feedback for Faster R-CNN** - intro: ECCV 2016. Carnegie Mellon University - paper: - poster: **An Implementation of Faster RCNN with Study for Region Sampling** - intro: Technical Report, 3 pages. CMU - arxiv: - github: - github: https://github.com/ruotianluo/pytorch-faster-rcnn **Interpretable R-CNN** - intro: North Carolina State University & Alibaba - keywords: AND-OR Graph (AOG) - arxiv: **Domain Adaptive Faster R-CNN for Object Detection in the Wild** - intro: CVPR 2018. ETH Zurich & ESAT/PSI - arxiv: ## Mask R-CNN - arxiv: - github(Keras): https://github.com/matterport/Mask_RCNN - github(Caffe2): https://github.com/facebookresearch/Detectron - github(Pytorch): - github(MXNet): https://github.com/TuSimple/mx-maskrcnn - github(Chainer): https://github.com/DeNA/Chainer_Mask_R-CNN ## Light-Head R-CNN **Light-Head R-CNN: In Defense of Two-Stage Object Detector** - intro: Tsinghua University & Megvii Inc - arxiv: - github(offical): https://github.com/zengarden/light_head_rcnn - github: ## Cascade R-CNN **Cascade R-CNN: Delving into High Quality Object Detection** - arxiv: - github: ## SPP-Net **Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition** - intro: ECCV 2014 / TPAMI 2015 - arxiv: - github: - notes: **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: - arxiv: **Object Detectors Emerge in Deep Scene CNNs** - intro: ICLR 2015 - arxiv: - paper: - paper: - slides: **segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection** - intro: CVPR 2015 - project(code+data): - arxiv: - github: **Object Detection Networks on Convolutional Feature Maps** - intro: TPAMI 2015 - keywords: NoC - arxiv: **Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction** - arxiv: - slides: - github: **DeepBox: Learning Objectness with Convolutional Networks** - keywords: DeepBox - arxiv: - github: ## YOLO **You Only Look Once: Unified, Real-Time Object Detection** [![img](https://camo.githubusercontent.com/e69d4118b20a42de4e23b9549f9a6ec6dbbb0814/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67)](https://camo.githubusercontent.com/e69d4118b20a42de4e23b9549f9a6ec6dbbb0814/687474703a2f2f706a7265646469652e636f6d2f6d656469612f66696c65732f6461726b6e65742d626c61636b2d736d616c6c2e706e67) - arxiv: - code: - github: - blog: - slides: - reddit: - github: - github: - github: - github: - github: - github: - github: - github: **darkflow - translate darknet to tensorflow. Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++** - blog: - github: **Start Training YOLO with Our Own Data** [![img](https://camo.githubusercontent.com/2f99b692dd7ce47d7832385f3e8a6654e680d92a/687474703a2f2f6775616e6768616e2e696e666f2f626c6f672f656e2f77702d636f6e74656e742f75706c6f6164732f323031352f31322f696d616765732d34302e6a7067)](https://camo.githubusercontent.com/2f99b692dd7ce47d7832385f3e8a6654e680d92a/687474703a2f2f6775616e6768616e2e696e666f2f626c6f672f656e2f77702d636f6e74656e742f75706c6f6164732f323031352f31322f696d616765732d34302e6a7067) - intro: train with customized data and class numbers/labels. Linux / Windows version for darknet. - blog: - github: **YOLO: Core ML versus MPSNNGraph** - intro: Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API. - blog: - github: **TensorFlow YOLO object detection on Android** - intro: Real-time object detection on Android using the YOLO network with TensorFlow - github: **Computer Vision in iOS – Object Detection** - blog: - github: ## YOLOv2 **YOLO9000: Better, Faster, Stronger** - arxiv: - code: https://pjreddie.com/darknet/yolov2/ - github(Chainer): - github(Keras): - github(PyTorch): - github(Tensorflow): - github(Windows): - github: - github: - github(TensorFlow): - github(Keras): - github(Keras): - github(TensorFlow): **darknet_scripts** - intro: Auxilary scripts to work with (YOLO) darknet deep learning famework. AKA -> How to generate YOLO anchors? - github: **Yolo_mark: GUI for marking bounded boxes of objects in images for training Yolo v2** - github: **LightNet: Bringing pjreddie's DarkNet out of the shadows** **YOLO v2 Bounding Box Tool** - intro: Bounding box labeler tool to generate the training data in the format YOLO v2 requires. - github: **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 **Object detection at 200 Frames Per Second** - intro: faster than Tiny-Yolo-v2 - arxiv: https://arxiv.org/abs/1805.06361 **Event-based Convolutional Networks for Object Detection in Neuromorphic Cameras** - intro: YOLE--Object Detection in Neuromorphic Cameras - arxiv:https://arxiv.org/abs/1805.07931 **OmniDetector: With Neural Networks to Bounding Boxes** - intro: a person detector on n fish-eye images of indoor scenes(NIPS 2018) - arxiv:https://arxiv.org/abs/1805.08503 - datasets:https://gitlab.com/omnidetector/omnidetector ## YOLOv3 **YOLOv3: An Incremental Improvement** - arxiv:https://arxiv.org/abs/1804.02767 - paper:https://pjreddie.com/media/files/papers/YOLOv3.pdf - code: - github(Official):https://github.com/pjreddie/darknet - github:https://github.com/mystic123/tensorflow-yolo-v3 - github:https://github.com/experiencor/keras-yolo3 - github:https://github.com/qqwweee/keras-yolo3 - github:https://github.com/marvis/pytorch-yolo3 - github:https://github.com/ayooshkathuria/pytorch-yolo-v3 - github:https://github.com/ayooshkathuria/YOLO_v3_tutorial_from_scratch - github:https://github.com/eriklindernoren/PyTorch-YOLOv3 - github:https://github.com/ultralytics/yolov3 - github:https://github.com/BobLiu20/YOLOv3_PyTorch - github:https://github.com/andy-yun/pytorch-0.4-yolov3 - github:https://github.com/DeNA/PyTorch_YOLOv3 ## YOLT **You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery** - intro: Small Object Detection - arxiv:https://arxiv.org/abs/1805.09512 - github:https://github.com/avanetten/yolt ## SSD **SSD: Single Shot MultiBox Detector** [![img](https://camo.githubusercontent.com/ad9b147ed3a5f48ffb7c3540711c15aa04ce49c6/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67)](https://camo.githubusercontent.com/ad9b147ed3a5f48ffb7c3540711c15aa04ce49c6/687474703a2f2f7777772e63732e756e632e6564752f7e776c69752f7061706572732f7373642e706e67) - intro: ECCV 2016 Oral - arxiv: - paper: - slides: [http://www.cs.unc.edu/%7Ewliu/papers/ssd_eccv2016_slide.pdf](http://www.cs.unc.edu/~wliu/papers/ssd_eccv2016_slide.pdf) - github(Official): - video: - github: - github: - github: - github: - github: - github(Caffe): **What's the diffience in performance between this new code you pushed and the previous code? #327** ## DSSD **DSSD : Deconvolutional Single Shot Detector** - intro: UNC Chapel Hill & Amazon Inc - arxiv: - github: - github: - demo: **Enhancement of SSD by concatenating feature maps for object detection** - intro: rainbow SSD (R-SSD) - arxiv: **Context-aware Single-Shot Detector** - keywords: CSSD, DiCSSD, DeCSSD, effective receptive fields (ERFs), theoretical receptive fields (TRFs) - arxiv: **Feature-Fused SSD: Fast Detection for Small Objects** ## FSSD **FSSD: Feature Fusion Single Shot Multibox Detector** **Weaving Multi-scale Context for Single Shot Detector** - intro: WeaveNet - keywords: fuse multi-scale information - arxiv: ## ESSD **Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network** **Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection** ## MDSSD **MDSSD: Multi-scale Deconvolutional Single Shot Detector for small objects** - arxiv: https://arxiv.org/abs/1805.07009 ## Pelee **Pelee: A Real-Time Object Detection System on Mobile Devices** https://github.com/Robert-JunWang/Pelee - intro: (ICLR 2018 workshop track) - arxiv: https://arxiv.org/abs/1804.06882 - github: https://github.com/Robert-JunWang/Pelee ## Fire SSD **Fire SSD: Wide Fire Modules based Single Shot Detector on Edge Device** - intro:low cost, fast speed and high mAP on factor edge computing devices - arxiv:https://arxiv.org/abs/1806.05363 ## R-FCN **R-FCN: Object Detection via Region-based Fully Convolutional Networks** - arxiv: - github: - github(MXNet): - github: - github: - github: - github: **R-FCN-3000 at 30fps: Decoupling Detection and Classification** **Recycle deep features for better object detection** - arxiv: ## FPN **Feature Pyramid Networks for Object Detection** - intro: Facebook AI Research - arxiv: **Action-Driven Object Detection with Top-Down Visual Attentions** - arxiv: **Beyond Skip Connections: Top-Down Modulation for Object Detection** - intro: CMU & UC Berkeley & Google Research - arxiv: **Wide-Residual-Inception Networks for Real-time Object Detection** - intro: Inha University - arxiv: **Attentional Network for Visual Object Detection** - intro: University of Maryland & Mitsubishi Electric Research Laboratories - arxiv: **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: **DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling** - intro: ICCV 2017 (poster) - arxiv: **Discriminative Bimodal Networks for Visual Localization and Detection with Natural Language Queries** - intro: CVPR 2017 - arxiv: **Spatial Memory for Context Reasoning in Object Detection** - arxiv: **Accurate Single Stage Detector Using Recurrent Rolling Convolution** - intro: CVPR 2017. SenseTime - keywords: Recurrent Rolling Convolution (RRC) - arxiv: - github: **Deep Occlusion Reasoning for Multi-Camera Multi-Target Detection** **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: **Point Linking Network for Object Detection** - intro: Point Linking Network (PLN) - arxiv: **Perceptual Generative Adversarial Networks for Small Object Detection** **Few-shot Object Detection** **Yes-Net: An effective Detector Based on Global Information** **SMC Faster R-CNN: Toward a scene-specialized multi-object detector** **Towards lightweight convolutional neural networks for object detection** **RON: Reverse Connection with Objectness Prior Networks for Object Detection** - intro: CVPR 2017 - arxiv: - github: **Mimicking Very Efficient Network for Object Detection** - intro: CVPR 2017. SenseTime & Beihang University - paper: **Residual Features and Unified Prediction Network for Single Stage Detection** **Deformable Part-based Fully Convolutional Network for Object Detection** - intro: BMVC 2017 (oral). Sorbonne Universités & CEDRIC - arxiv: **Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors** - intro: ICCV 2017 - arxiv: **Recurrent Scale Approximation for Object Detection in CNN** - intro: ICCV 2017 - keywords: Recurrent Scale Approximation (RSA) - arxiv: - github: ## DSOD **DSOD: Learning Deeply Supervised Object Detectors from Scratch** ![img](https://user-images.githubusercontent.com/3794909/28934967-718c9302-78b5-11e7-89ee-8b514e53e23c.png) - intro: ICCV 2017. Fudan University & Tsinghua University & Intel Labs China - arxiv: - github: - github:https://github.com/Windaway/DSOD-Tensorflow - github:https://github.com/chenyuntc/dsod.pytorch **Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids** - arxiv:https://arxiv.org/abs/1712.00886 - github:https://github.com/szq0214/GRP-DSOD **Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages** - intro: BMVC 2018 - arXiv: https://arxiv.org/abs/1807.11013 **Object Detection from Scratch with Deep Supervision** - intro: This is an extended version of DSOD - arXiv: 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: **CoupleNet: Coupling Global Structure with Local Parts for Object Detection** - intro: ICCV 2017 - arxiv: **Incremental Learning of Object Detectors without Catastrophic Forgetting** - intro: ICCV 2017. Inria - arxiv: **Zoom Out-and-In Network with Map Attention Decision for Region Proposal and Object Detection** **StairNet: Top-Down Semantic Aggregation for Accurate One Shot Detection** **Dynamic Zoom-in Network for Fast Object Detection in Large Images** **Zero-Annotation Object Detection with Web Knowledge Transfer** - intro: NTU, Singapore & Amazon - keywords: multi-instance multi-label domain adaption learning framework - arxiv: ## MegDet **MegDet: A Large Mini-Batch Object Detector** - intro: Peking University & Tsinghua University & Megvii Inc - arxiv: **Receptive Field Block Net for Accurate and Fast Object Detection** - intro: RFBNet - arxiv: - github: **An Analysis of Scale Invariance in Object Detection - SNIP** - arxiv: - github: **Feature Selective Networks for Object Detection** **Learning a Rotation Invariant Detector with Rotatable Bounding Box** - arxiv: - github: **Scalable Object Detection for Stylized Objects** - intro: Microsoft AI & Research Munich - arxiv: **Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids** - arxiv: - github: **Deep Regionlets for Object Detection** - keywords: region selection network, gating network - arxiv: **Training and Testing Object Detectors with Virtual Images** - intro: IEEE/CAA Journal of Automatica Sinica - arxiv: **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: **Spot the Difference by Object Detection** - intro: Tsinghua University & JD Group - arxiv: **Localization-Aware Active Learning for Object Detection** - arxiv: **Object Detection with Mask-based Feature Encoding** - arxiv: **LSTD: A Low-Shot Transfer Detector for Object Detection** - intro: AAAI 2018 - arxiv: **Pseudo Mask Augmented Object Detection** **Revisiting RCNN: On Awakening the Classification Power of Faster RCNN** **Learning Region Features for Object Detection** - intro: Peking University & MSRA - arxiv: **Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection** - intro: Singapore Management University & Zhejiang University - arxiv: **Object Detection for Comics using Manga109 Annotations** - intro: University of Tokyo & National Institute of Informatics, Japan - arxiv: **Task-Driven Super Resolution: Object Detection in Low-resolution Images** - arxiv: **Transferring Common-Sense Knowledge for Object Detection** - arxiv: **Multi-scale Location-aware Kernel Representation for Object Detection** - intro: CVPR 2018 - arxiv: - github: **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 **Robust Physical Adversarial Attack on Faster R-CNN Object Detector** - arxiv: https://arxiv.org/abs/1804.05810 ## RefineNet **Single-Shot Refinement Neural Network for Object Detection** - intro: CVPR 2018 - arxiv: - github: - github: https://github.com/lzx1413/PytorchSSD - github: https://github.com/ddlee96/RefineDet_mxnet - github: https://github.com/MTCloudVision/RefineDet-Mxnet ## DetNet **DetNet: A Backbone network for Object Detection** - intro: Tsinghua University & Face++ - arxiv: https://arxiv.org/abs/1804.06215 ## SSOD **Self-supervisory Signals for Object Discovery and Detection** - Google Brain - arxiv:https://arxiv.org/abs/1806.03370 ## CornerNet **CornerNet: Detecting Objects as Paired Keypoints** - intro: ECCV 2018 - arXiv: https://arxiv.org/abs/1808.01244 - github: ## M2Det **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 ## 3D Object Detection **3D Backbone Network for 3D Object Detection** - arXiv: https://arxiv.org/abs/1901.08373 **LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDARs** - arxiv: https://arxiv.org/abs/1805.04902 - github: https://github.com/CPFL/Autoware/tree/feature/cnn_lidar_detection ## ZSD(Zero-Shot Object Detection) **Zero-Shot Detection** - intro: Australian National University - keywords: YOLO - arxiv: **Zero-Shot Object Detection** - arxiv: 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 **Zero-Shot Object Detection by Hybrid Region Embedding** - arxiv: https://arxiv.org/abs/1805.06157 ## OSD(One-Shot Object Detection) **Comparison Network for One-Shot Conditional Object Detection** - arXiv: https://arxiv.org/abs/1904.02317 **One-Shot Object Detection** RepMet: Representative-based metric learning for classification and one-shot object detection - intro: IBM Research AI - arxiv:https://arxiv.org/abs/1806.04728 - github: TODO ## Weakly Supervised Object Detection **Weakly Supervised Object Detection in Artworks** - intro: ECCV 2018 Workshop Computer Vision for Art Analysis - arXiv: https://arxiv.org/abs/1810.02569 - Datasets: https://wsoda.telecom-paristech.fr/downloads/dataset/IconArt_v1.zip **Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation** - intro: CVPR 2018 - arXiv: https://arxiv.org/abs/1803.11365 - homepage: https://naoto0804.github.io/cross_domain_detection/ - paper: http://openaccess.thecvf.com/content_cvpr_2018/html/Inoue_Cross-Domain_Weakly-Supervised_Object_CVPR_2018_paper.html - github: https://github.com/naoto0804/cross-domain-detection ## Softer-NMS **《Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection》** - intro: CMU & Face++ - arXiv: https://arxiv.org/abs/1809.08545 - github: https://github.com/yihui-he/softer-NMS ## 2019 **Feature Selective Anchor-Free Module for Single-Shot Object Detection** - intro: CVPR 2019 - arXiv: https://arxiv.org/abs/1903.00621 **Object Detection based on Region Decomposition and Assembly** - intro: AAAI 2019 - arXiv: https://arxiv.org/abs/1901.08225 **Bottom-up Object Detection by Grouping Extreme and Center Points** - intro: one stage 43.2% on COCO test-dev - arXiv: https://arxiv.org/abs/1901.08043 - github: https://github.com/xingyizhou/ExtremeNet **ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features** - intro: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING - arXiv: https://arxiv.org/abs/1901.07925 **Consistent Optimization for Single-Shot Object Detection** - intro: improves RetinaNet from 39.1 AP to 40.1 AP on COCO datase - arXiv: https://arxiv.org/abs/1901.06563 **Learning Pairwise Relationship for Multi-object Detection in Crowded Scenes** - arXiv: https://arxiv.org/abs/1901.03796 **RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free** - arXiv: https://arxiv.org/abs/1901.03353 - github: https://github.com/chengyangfu/retinamask **Region Proposal by Guided Anchoring** - intro: CUHK - SenseTime Joint Lab - arXiv: https://arxiv.org/abs/1901.03278 **Scale-Aware Trident Networks for Object Detection** - intro: mAP of **48.4** on the COCO dataset - arXiv: https://arxiv.org/abs/1901.01892 ## 2018 **Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions** - arXiv: https://arxiv.org/abs/1812.11901 **Strong-Weak Distribution Alignment for Adaptive Object Detection** - arXiv: https://arxiv.org/abs/1812.04798 **AutoFocus: Efficient Multi-Scale Inference** - intro: AutoFocus obtains an **mAP of 47.9%** (68.3% at 50% overlap) on the **COCO test-dev** set while processing **6.4 images per second on a Titan X (Pascal) GPU** - arXiv: https://arxiv.org/abs/1812.01600 **NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection** - intro: Google Could - arXiv: https://arxiv.org/abs/1812.00124 **SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection** - intro: UC Berkeley - arXiv: https://arxiv.org/abs/1812.00929 **Grid R-CNN** - intro: SenseTime - arXiv: https://arxiv.org/abs/1811.12030 **Deformable ConvNets v2: More Deformable, Better Results** - intro: Microsoft Research Asia - arXiv: https://arxiv.org/abs/1811.11168 **Anchor Box Optimization for Object Detection** - intro: Microsoft Research - arXiv: https://arxiv.org/abs/1812.00469 **Efficient Coarse-to-Fine Non-Local Module for the Detection of Small Objects** - intro: https://arxiv.org/abs/1811.12152 **NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection** - arXiv: https://arxiv.org/abs/1812.00124 **Learning RoI Transformer for Detecting Oriented Objects in Aerial Images** - arXiv: https://arxiv.org/abs/1812.00155 **Integrated Object Detection and Tracking with Tracklet-Conditioned Detection** - intro: Microsoft Research Asia - arXiv: https://arxiv.org/abs/1811.11167 **Deep Regionlets: Blended Representation and Deep Learning for Generic Object Detection** - arXiv: https://arxiv.org/abs/1811.11318 **Gradient Harmonized Single-stage Detector** - intro: AAAI 2019 - arXiv: https://arxiv.org/abs/1811.05181 **CFENet: Object Detection with Comprehensive Feature Enhancement Module** - intro: ACCV 2018 - github: https://github.com/qijiezhao/CFENet **DeRPN: Taking a further step toward more general object detection** - intro: AAAI 2019 - arXiv: https://arxiv.org/abs/1811.06700 - github: https://github.com/HCIILAB/DeRPN **Hybrid Knowledge Routed Modules for Large-scale Object Detection** - intro: Sun Yat-Sen University & Huawei Noah’s Ark Lab - arXiv: https://arxiv.org/abs/1810.12681 - github: https://github.com/chanyn/HKRM **《Receptive Field Block Net for Accurate and Fast Object Detection》** - intro: ECCV 2018 - arXiv: [https://arxiv.org/abs/1711.07767](https://arxiv.org/abs/1711.07767) - github: [https://github.com/ruinmessi/RFBNet](https://github.com/ruinmessi/RFBNet) **Deep Feature Pyramid Reconfiguration for Object Detection** - intro: ECCV 2018 - arXiv: https://arxiv.org/abs/1808.07993 **Unsupervised Hard Example Mining from Videos for Improved Object Detection** - intro: ECCV 2018 - arXiv: https://arxiv.org/abs/1808.04285 **Acquisition of Localization Confidence for Accurate Object Detection** - intro: ECCV 2018 - arXiv: https://arxiv.org/abs/1807.11590 - github: https://github.com/vacancy/PreciseRoIPooling **Toward Scale-Invariance and Position-Sensitive Region Proposal Networks** - intro: ECCV 2018 - arXiv: https://arxiv.org/abs/1807.09528 **MetaAnchor: Learning to Detect Objects with Customized Anchors** - arxiv: https://arxiv.org/abs/1807.00980 **Relation Network for Object Detection** - intro: CVPR 2018 - arxiv: https://arxiv.org/abs/1711.11575 - github:https://github.com/msracver/Relation-Networks-for-Object-Detection **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 **Learning Rich Features for Image Manipulation Detection** - intro: CVPR 2018 Camera Ready - arxiv: https://arxiv.org/abs/1805.04953 **SNIPER: Efficient Multi-Scale Training** - arxiv:https://arxiv.org/abs/1805.09300 - github:https://github.com/mahyarnajibi/SNIPER **Soft Sampling for Robust Object Detection** - intro: the robustness of object detection under the presence of missing annotations - arxiv:https://arxiv.org/abs/1806.06986 **Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria** - intro: TNNLS 2018 - arxiv:https://arxiv.org/abs/1807.00147 - code: http://kezewang.com/codes/ASM_ver1.zip ## Other **R3-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos** - arxiv: https://arxiv.org/abs/1808.05560 - youtube: https://youtu.be/xCYD-tYudN0 # Detection Toolbox - [Detectron(FAIR)](https://github.com/facebookresearch/Detectron): Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework. - [Detectron2](https://github.com/facebookresearch/detectron2): Detectron2 is FAIR's next-generation research platform for object detection and segmentation. - [maskrcnn-benchmark(FAIR)](https://github.com/facebookresearch/maskrcnn-benchmark): Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch. - [mmdetection(SenseTime&CUHK)](https://github.com/open-mmlab/mmdetection): mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by [Multimedia Laboratory, CUHK](http://mmlab.ie.cuhk.edu.hk/).