paper整理-deep learning object detection

# paper整理-deep learning object detection --- > 處理時間:2022/03/10 > forked from [xialeiliu/Awesome-Incremental-Learning](https://github.com/hoya012/deep_learning_object_detection) --- --- ## 2014 - **重要** **[R-CNN]** Rich feature hierarchies for accurate object detection and semantic segmentation | **[CVPR' 14]** |[`[pdf]`](https://arxiv.org/pdf/1311.2524.pdf) [`[official code - caffe]`](https://github.com/rbgirshick/rcnn) - **重要** **[OverFeat]** OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | **[ICLR' 14]** |[`[pdf]`](https://arxiv.org/pdf/1312.6229.pdf) [`[official code - torch]`](https://github.com/sermanet/OverFeat) - **重要** **[SPPnet]** Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | |[`[pdf]`](https://arxiv.org/abs/1406.4729) - **重要-影像分類** **[ResNet]** Deep Residual Learning for Image Recognition [`[pdf]`](https://arxiv.org/abs/1512.03385) - **重要-影像分類** **[VGG]** Very Deep Convolutional Networks for Large-Scale Image Recognition [`[pdf]`](https://arxiv.org/abs/1409.1556) --- ## 2015 - **重要** **[Fast R-CNN]** Fast R-CNN | **[ICCV' 15]** |[`[pdf]`](https://arxiv.org/pdf/1504.08083.pdf) [`[official code - caffe]`](https://github.com/rbgirshick/fast-rcnn) - **重要** **[Faster R-CNN, RPN]** Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | **[NIPS' 15]** |[`[pdf]`](https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf) [`[official code - caffe]`](https://github.com/rbgirshick/py-faster-rcnn) [`[unofficial code - tensorflow]`](https://github.com/endernewton/tf-faster-rcnn) [`[unofficial code - pytorch]`](https://github.com/jwyang/faster-rcnn.pytorch) --- ## 2016 - **重要** **[YOLO v1]** You Only Look Once: Unified, Real-Time Object Detection | **[CVPR' 16]** |[`[pdf]`](https://arxiv.org/pdf/1506.02640.pdf) [`[official code - c]`](https://pjreddie.com/darknet/yolo/) - **重要** **[OHEM]** Training Region-based Object Detectors with Online Hard Example Mining | **[CVPR' 16]** |[`[pdf]`](https://arxiv.org/pdf/1604.03540.pdf) [`[official code - caffe]`](https://github.com/abhi2610/ohem) - **重要** **[SSD]** SSD: Single Shot MultiBox Detector | **[ECCV' 16]** |[`[pdf]`](https://arxiv.org/pdf/1512.02325.pdf) [`[official code - caffe]`](https://github.com/weiliu89/caffe/tree/ssd) [`[unofficial code - tensorflow]`](https://github.com/balancap/SSD-Tensorflow) [`[unofficial code - pytorch]`](https://github.com/amdegroot/ssd.pytorch) - **重要** **[R-FCN]** R-FCN: Object Detection via Region-based Fully Convolutional Networks | **[NIPS' 16]** |[`[pdf]`](https://arxiv.org/pdf/1605.06409.pdf) [`[official code - caffe]`](https://github.com/daijifeng001/R-FCN) [`[unofficial code - caffe]`](https://github.com/YuwenXiong/py-R-FCN) --- ## 2017 - **重要** **[FPN]** Feature Pyramid Networks for Object Detection | **[CVPR' 17]** |[`[pdf]`](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lin_Feature_Pyramid_Networks_CVPR_2017_paper.pdf) [`[unofficial code - caffe]`](https://github.com/unsky/FPN) - **重要** **[YOLO v2]** YOLO9000: Better, Faster, Stronger | **[CVPR' 17]** |[`[pdf]`](https://arxiv.org/pdf/1612.08242.pdf) [`[official code - c]`](https://pjreddie.com/darknet/yolo/) [`[unofficial code - caffe]`](https://github.com/quhezheng/caffe_yolo_v2) [`[unofficial code - tensorflow]`](https://github.com/nilboy/tensorflow-yolo) [`[unofficial code - tensorflow]`](https://github.com/sualab/object-detection-yolov2) [`[unofficial code - pytorch]`](https://github.com/longcw/yolo2-pytorch) - **重要** **[RetinaNet]** Focal Loss for Dense Object Detection | **[ICCV' 17]** |[`[pdf]`](https://arxiv.org/pdf/1708.02002.pdf) [`[official code - keras]`](https://github.com/fizyr/keras-retinanet) [`[unofficial code - pytorch]`](https://github.com/kuangliu/pytorch-retinanet) [`[unofficial code - mxnet]`](https://github.com/unsky/RetinaNet) [`[unofficial code - tensorflow]`](https://github.com/tensorflow/tpu/tree/master/models/official/retinanet) - **[SMN]** Spatial Memory for Context Reasoning in Object Detection | **[ICCV' 17]** |[`[pdf]`](http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Spatial_Memory_for_ICCV_2017_paper.pdf) 、引用次數133 - **[Light-Head R-CNN]** Light-Head R-CNN: In Defense of Two-Stage Object Detector | **[arXiv' 17]** |[`[pdf]`](https://arxiv.org/pdf/1711.07264.pdf) [`[official code - tensorflow]`](https://github.com/zengarden/light_head_rcnn) 、引用次數261 - **重要** **[Soft-NMS]** Improving Object Detection With One Line of Code | **[ICCV' 17]** |[`[pdf]`](https://arxiv.org/pdf/1704.04503.pdf) [`[official code - caffe]`](https://github.com/bharatsingh430/soft-nms)、引用次數1055 - **重要-SSD家族** **[DSSD]** DSSD : Deconvolutional Single Shot Detector | [`[pdf]`](https://arxiv.org/pdf/1704.04503.pdf) 、引用次數1570 - **重要-SSD家族** **[FSSD]** FSSD: Feature Fusion Single Shot Multibox Detector | [`[pdf]`](https://arxiv.org/abs/1712.00960) 、引用次數308 ## 2018 - **重要** **[YOLO v3]** YOLOv3: An Incremental Improvement | **[arXiv' 18]** |[`[pdf]`](https://pjreddie.com/media/files/papers/YOLOv3.pdf) [`[official code - c]`](https://pjreddie.com/darknet/yolo/) [`[unofficial code - pytorch]`](https://github.com/ayooshkathuria/pytorch-yolo-v3) [`[unofficial code - pytorch]`](https://github.com/eriklindernoren/PyTorch-YOLOv3) [`[unofficial code - keras]`](https://github.com/qqwweee/keras-yolo3) [`[unofficial code - tensorflow]`](https://github.com/mystic123/tensorflow-yolo-v3) - **重要** **[RefineDet-SSD家族]** Single-Shot Refinement Neural Network for Object Detection | **[CVPR' 18]** |[`[pdf]`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Single-Shot_Refinement_Neural_CVPR_2018_paper.pdf) [`[official code - caffe]`](https://github.com/sfzhang15/RefineDet) [`[unofficial code - chainer]`](https://github.com/fukatani/RefineDet_chainer) [`[unofficial code - pytorch]`](https://github.com/lzx1413/PytorchSSD)、引用次數1055 - **重要** **[Cascade R-CNN]** Cascade R-CNN: Delving into High Quality Object Detection | **[CVPR' 18]** |[`[pdf]`](http://openaccess.thecvf.com/content_cvpr_2018/papers/Cai_Cascade_R-CNN_Delving_CVPR_2018_paper.pdf) [`[official code - caffe]`](https://github.com/zhaoweicai/cascade-rcnn) 、引用次數2129 - **重要** **[CornerNet]** CornerNet: Detecting Objects as Paired Keypoints | **[ECCV' 18]** |[`[pdf]`](https://arxiv.org/pdf/1808.01244.pdf) [`[official code - pytorch]`](https://github.com/princeton-vl/CornerNet)、引用次數1806 - **[Softer-NMS]** Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | **[arXiv' 18]** |[`[pdf]`](https://arxiv.org/pdf/1809.08545.pdf) 、引用次數259 --- ## 2019 - **重要** **[M2Det]** M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | **[AAAI' 19]** |[`[pdf]`](https://arxiv.org/pdf/1811.04533.pdf) [`[official code - pytorch]`](https://github.com/qijiezhao/M2Det) 、引用次數436 - **重要** **[GIoU]** Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | **[CVPR' 19]** |[`[pdf]`](https://arxiv.org/pdf/1902.09630.pdf) 、引用次數1233 - **重要** **[Libra R-CNN]** Libra R-CNN: Towards Balanced Learning for Object Detection | **[CVPR' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.02701.pdf) 、引用次數613 - **重要** **[NAS-FPN]** NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | **[CVPR' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.07392.pdf) 、引用次數697 - **[Adaptive NMS]** Adaptive NMS: Refining Pedestrian Detection in a Crowd | **[CVPR' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.03629.pdf) 、引用次數152 - Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | **[CVPR' 19]** |[`[pdf]`](https://arxiv.org/pdf/1905.05396.pdf)、引用次數138 - Multi-adversarial Faster-RCNN for Unrestricted Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1907.10343v1.pdf)、引用次數127 - A Robust Learning Approach to Domain Adaptive Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.02361.pdf) 、引用次數103 - Selectivity or Invariance: Boundary-Aware Salient Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1812.10066.pdf) 、引用次數104 - **重要** **[TridentNet]** Scale-Aware Trident Networks for Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1901.01892.pdf) 、引用次數504 - **重要** **[CenterNet]** CenterNet: Keypoint Triplets for Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.08189.pdf)、引用次數962 - **[Auto-FPN]** Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification | **[ICCV' 19]** |[`[pdf]`](http://openaccess.thecvf.com/content_ICCV_2019/papers/Xu_Auto-FPN_Automatic_Network_Architecture_Adaptation_for_Object_Detection_Beyond_Classification_ICCV_2019_paper.pdf) 、引用次數104 - **[ThunderNet]** ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1903.11752.pdf)、引用次數142 - **[RDN]** Relation Distillation Networks for Video Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1908.09511.pdf)、引用次數99 - **[SCAN]** Stacked Cross Refinement Network for Edge-Aware Salient Object Detection | **[ICCV' 19]** |[`[official code]`](https://github.com/wuzhe71/SCAN) |[`[pdf]`](https://openaccess.thecvf.com/content_ICCV_2019/html/Wu_Stacked_Cross_Refinement_Network_for_Edge-Aware_Salient_Object_Detection_ICCV_2019_paper.html)、引用次數164 - **[ClusDet]** Clustered Object Detection in Aerial Images | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.08008.pdf)、引用次數100 - Few-Shot Object Detection via Feature Reweighting | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1812.01866.pdf) 、引用次數281 - **[Objects365]** Objects365: A Large-Scale, High-Quality Dataset for Object Detection | **[ICCV' 19]** |[`[pdf]`](http://openaccess.thecvf.com/content_ICCV_2019/papers/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.pdf) 、引用次數104 - **重要** **[EGNet]** EGNet: Edge Guidance Network for Salient Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1908.08297.pdf) 、引用次數*400* - **重要****[FCOS]** FCOS: Fully Convolutional One-Stage Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.01355.pdf) 、引用次數**1694** - **[RepPoints]** RepPoints: Point Set Representation for Object Detection | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.11490.pdf) 、引用次數**318** - Meta-Learning to Detect Rare Objects | **[ICCV' 19]** |[`[pdf]`](http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf)、引用次數117 - **[Gaussian YOLOv3]** Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | **[ICCV' 19]** |[`[pdf]`](https://arxiv.org/pdf/1904.04620.pdf) [`[official code - c]`](https://github.com/jwchoi384/Gaussian_YOLOv3) 、引用次數204 - **[FreeAnchor]** FreeAnchor: Learning to Match Anchors for Visual Object Detection | **[NeurIPS' 19]** |[`[pdf]`](https://arxiv.org/pdf/1909.02466v1.pdf) 、引用次數169 - **[DetNAS]** DetNAS: Backbone Search for Object Detection | **[NeurIPS' 19]** |[`[pdf]`](https://arxiv.org/pdf/1903.10979v4.pdf) 、引用次數137 - **[AA]** Learning Data Augmentation Strategies for Object Detection | **[arXiv' 19]** |[`[pdf]`](https://arxiv.org/pdf/1906.11172.pdf) 、引用次數241 - **[Spinenet]** Spinenet: Learning scale-permuted backbone for recognition and localization | **[arXiv' 19]** |[`[pdf]`](https://arxiv.org/pdf/1912.05027.pdf) 、引用次數111 - **重要** Object Detection in 20 Years: A Survey | **[arXiv' 19]** |[`[pdf]`](https://arxiv.org/pdf/1905.05055.pdf) 、引用次數508 - Salient Object Detection in the Deep Learning Era: An In-Depth Survey | **[CVPR 19]** |[`[pdf]`](https://arxiv.org/abs/1904.09146) 、引用次數288 - **重要** **[CSPNet]** CSPNet: A New Backbone that can Enhance Learning Capability of CNN [`[pdf]`](https://arxiv.org/abs/1911.11929) 、引用次數736 --- ## 2020 - **[CBnet]** Cbnet: A novel composite backbone network architecture for object detection | **[AAAI' 20]** |[`[pdf]`](https://arxiv.org/pdf/1909.03625.pdf) 、引用次數144 - **重要** **[Distance-IoU Loss]** Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | **[AAAI' 20]** |[`[pdf]`](https://arxiv.org/pdf/1911.08287v1.pdf) 、引用次數594 - **重要** **[YOLOv4]** YOLOv4: Optimal Speed and Accuracy of Object Detection | **[arXiv' 20]** |[`[pdf]`](https://arxiv.org/pdf/2004.10934.pdf) 、引用次數3206 - **重要** **[Scaled-YOLOv4]** Scaled-YOLOv4: Scaling Cross Stage Partial Network、[`[pdf]`](https://arxiv.org/pdf/2004.10934.pdf) 、引用次數212 - **[PP YOLO]** PP-YOLO: An Effective and Efficient Implementation of Object Detector |[`[pdf]`](https://arxiv.org/abs/2007.12099) 、引用次數57 - Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1908.01998.pdf) 、引用次數188 - **重要** Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1912.02424.pdf) 、引用次數369 - Rethinking Classification and Localization for Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1904.06493.pdf) 、引用次數133 - **重要** **[EfficientDet]** EfficientDet: Scalable and Efficient Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1911.09070.pdf) 、引用次數1498 - Dynamic Refinement Network for Oriented and Densely Packed Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/2005.09973.pdf) 、引用次數76 - **[D2Det]** D2Det: Towards High Quality Object Detection and Instance Segmentation | **[CVPR' 20]** |[`[pdf]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.pdf) 、引用次數71 - Prime Sample Attention in Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1904.04821.pdf) 、引用次數88 - Exploring Categorical Regularization for Domain Adaptive Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/2003.09152.pdf) 、引用次數79 - **[NAS-FCOS]** NAS-FCOS: Fast Neural Architecture Search for Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1906.04423.pdf) 、引用次數79 - **[AugFPN]** AugFPN: Improving Multi-Scale Feature Learning for Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/1912.05384.pdf) 、引用次數113 - Incremental Few-Shot Object Detection | **[CVPR' 20]** |[`[pdf]`](https://arxiv.org/pdf/2003.04668.pdf) 、引用次數90 - DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution | **[arXiv' 20]** |[`[pdf]`](https://arxiv.org/pdf/2006.02334v1.pdf) 、引用次數198 - **重要** **[DETR]** End-to-End Object Detection with Transformers | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2005.12872.pdf) 、引用次數1644 - Suppress and Balance: A Simple Gated Network for Salient Object Detection | **[ECCV' 20]** |[`[code]`](https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency) 、引用次數111 - **[Chained-Tracker]** Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2007.14557.pdf) -引用次數81 - Highly Efficient Salient Object Detection with 100K Parameters | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2003.05643.pdf) -引用次數63 - Arbitrary-Oriented Object Detection with Circular Smooth Label | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2003.05597.pdf) -引用次數87 - Soft Anchor-Point Object Detection | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/1911.12448.pdf) -引用次數69 - **[Dynamic R-CNN]** Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2004.06002.pdf) -引用次數91 - Multi-Scale Positive Sample Refinement for Few-Shot Object Detection | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2007.09384.pdf) 、引用次數53 - Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2007.12107.pdf) 、引用次數60 - Pillar-based Object Detection for Autonomous Driving | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2007.10323.pdf) 、引用次數52 - Probabilistic Anchor Assignment with IoU Prediction for Object Detection | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/pdf/2007.08103.pdf) 、引用次數76 - On the Importance of Data Augmentation for Object Detection | **[ECCV' 20]** |[`[pdf]`](https://arxiv.org/abs/1906.11172) 、引用次數53 --- ### 2021 - **[Generalized Focal Loss V2]** Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection [`[pdf]`]()、引用次數35 - **[Sparse R-CNN]** Sparse R-CNN: End-to-End Object Detection with Learnable Proposals [`[pdf]`](https://arxiv.org/abs/2011.12450)、引用次數107 - **[Center-based 3D]** Center-based 3D Object Detection and Tracking [`[pdf]`](https://arxiv.org/abs/2006.11275)、引用次數141 - **[YOLOR]** You Only Learn One Representation: Unified Network for Multiple Tasks [`[pdf]`](https://arxiv.org/abs/2105.04206)、引用次數32 --- >補充 ### 行人檢測-整理 https://github.com/xingkongliang/Pedestrian-Detection ---

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