计算机视觉论文总结系列：目标检测篇

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目标检测算法分类

基于深度学习的目标检测算法主要分为两类：

1.Two stage目标检测算法

​ 先进行区域生成（region proposal，RP）（一个有可能包含待检物体的预选框），再通过卷积神经网络进行样本分类。

​ 任务：特征提取—>生成RP—>分类/定位回归。

​ 常见的two stage目标检测算法有：R-CNN、SPP-Net、Fast R-CNN、Faster R-CNN和R-FCN等。

2.One stage目标检测算法

​ 不用RP，直接在网络中提取特征来预测物体分类和位置。

​ 任务：特征提取—>分类/定位回归。

​ 常见的one stage目标检测算法有：OverFeat、YOLOv1、YOLOv2、YOLOv3、SSD和RetinaNet等。

目标检测技术发展

目标检测常用数据集

  • [PASCAL VOC] The PASCAL Visual Object Classes (VOC) Challenge | [IJCV’ 10] | [pdf]
  • [PASCAL VOC] The PASCAL Visual Object Classes Challenge: A Retrospective | [IJCV’ 15] | [pdf] | [link]
  • [ImageNet] ImageNet: A Large-Scale Hierarchical Image Database| [CVPR’ 09] | [pdf]
  • [ImageNet] ImageNet Large Scale Visual Recognition Challenge | [IJCV’ 15] | [pdf] | [link]
  • [COCO] Microsoft COCO: Common Objects in Context | [ECCV’ 14] | [pdf] | [link]
  • [Open Images] The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale | [arXiv’ 18] | [pdf] | [link]
  • [DOTA] DOTA: A Large-scale Dataset for Object Detection in Aerial Images | [CVPR’ 18] | [pdf] | [link]
  • [Objects365] Objects365: A Large-Scale, High-Quality Dataset for Object Detection | [ICCV’ 19] | [[link]](

目标检测论文

2014论文及代码

2015论文及代码

2016论文及代码

  • [YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR’ 16] |[pdf] [official code - c]
  • [G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR’ 16] |[pdf]
  • [AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR’ 16] |[pdf]
  • [ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR’ 16] |[pdf]
  • [HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR’ 16] |[pdf]
  • [OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR’ 16] |[pdf] [official code - caffe]
  • [CRAPF] CRAFT Objects from Images | [CVPR’ 16] |[pdf] [official code - caffe]
  • [MPN] A MultiPath Network for Object Detection | [BMVC’ 16] |[pdf] [official code - torch]
  • [SSD] SSD: Single Shot MultiBox Detector | [ECCV’ 16] |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch]
  • [GBDNet] Crafting GBD-Net for Object Detection | [ECCV’ 16] |[pdf] [official code - caffe]
  • [CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV’ 16] |[pdf]
  • [MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV’ 16] |[pdf] [official code - caffe]
  • [R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS’ 16] |[pdf] [official code - caffe] [unofficial code - caffe]
  • [PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW’ 16] |[pdf] [official code - caffe]
  • [DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI’ 16] |[pdf]
  • [NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI’ 16] |[pdf]

2017论文及代码

2018论文及代码

  • [YOLO v3] YOLOv3: An Incremental Improvement | [arXiv’ 18] |[pdf] [official code - c] [unofficial code - pytorch] [unofficial code - pytorch] [unofficial code - keras] [unofficial code - tensorflow]
  • [ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV’ 18] |[pdf] [official code - caffe]
  • [SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR’ 18] |[pdf] [official code - tensorflow]
  • [STDN] Scale-Transferrable Object Detection | [CVPR’ 18] |[pdf]
  • [RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR’ 18] |[pdf] [official code - caffe] [unofficial code - chainer] [unofficial code - pytorch]
  • [MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR’ 18] |[pdf]
  • [DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR’ 18] |[pdf] [official code - caffe]
  • [SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR’ 18] |[pdf]
  • [Relation-Network] Relation Networks for Object Detection | [CVPR’ 18] |[pdf] [official code - mxnet]
  • [Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR’ 18] |[pdf] [official code - caffe]
  • Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR’ 18] |[[pdf]](https://ivul.kaust.edu.sa/Documents/Publications/2018/Finding Tiny Faces in the Wild with Generative Adversarial Network.pdf)
  • [MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR’ 18] |[pdf] [official code - caffe]
  • Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR’ 18] |[pdf] [official code - chainer]
  • [Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR’ 18] |[pdf]
  • [STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC’ 18] |[pdf]
  • [RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV’ 18] |[pdf] [official code - pytorch]
  • Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV’ 18] |[pdf]
  • [CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV’ 18] |[pdf] [official code - pytorch]
  • [PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV’ 18] |[pdf]
  • [Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv’ 18] |[pdf]
  • [ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD’ 18] |[pdf] [official code - tensorflow]
  • [Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS’ 18] |[pdf] [official code - caffe]
  • [HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS’ 18] |[pdf]
  • [MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS’ 18] |[pdf]
  • [SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS’ 18] |[pdf]

2019论文及代码

  • [M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI’ 19] |[pdf] [official code - pytorch]
  • [R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI’ 19] |[pdf]
  • [CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR’ 19] |[pdf]
  • Feature Intertwiner for Object Detection | [ICLR’ 19] |[pdf]
  • [GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR’ 19] |[pdf]
  • Automatic adaptation of object detectors to new domains using self-training | [CVPR’ 19] |[pdf]
  • [Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR’ 19] |[pdf]
  • [FSAF] Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR’ 19] |[pdf]
  • [ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR’ 19] |[pdf] | [official code - pytorch]
  • [C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
    | [CVPR’ 19] |[pdf] | [official code - torch]
  • [ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR’ 19] |[pdf]
  • Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR’ 19] |[pdf] | [official code - caffe2]
  • Activity Driven Weakly Supervised Object Detection | [CVPR’ 19] |[pdf]
  • Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR’ 19] |[pdf]
  • Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR’ 19] |[pdf] | [official code - pytorch]
  • [NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR’ 19] |[pdf]
  • [Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR’ 19] |[pdf]
  • Point in, Box out: Beyond Counting Persons in Crowds | [CVPR’ 19] |[pdf]
  • Locating Objects Without Bounding Boxes | [CVPR’ 19] |[pdf]
  • Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR’ 19] |[pdf]
  • Towards Universal Object Detection by Domain Attention | [CVPR’ 19] |[pdf]
  • Exploring the Bounds of the Utility of Context for Object Detection | [CVPR’ 19] |[pdf]
  • What Object Should I Use? - Task Driven Object Detection | [CVPR’ 19] |[pdf]
  • Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR’ 19] |[pdf]
  • Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR’ 19] |[pdf]
  • Fully Quantized Network for Object Detection | [CVPR’ 19] |[pdf]
  • Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR’ 19] |[pdf]
  • Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR’ 19] |[pdf]
  • [Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR’ 19] |[pdf]
  • Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR’ 19] |[pdf]
  • Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR’ 19] |[pdf]
  • Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR’ 19] |[pdf]
  • [MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR’ 19] |[pdf]
  • You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR’ 19] |[pdf]
  • Object detection with location-aware deformable convolution and backward attention filtering | [CVPR’ 19] |[pdf]
  • Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR’ 19] |[pdf]
  • [GFR] Improving Object Detection from Scratch via Gated Feature Reuse | [BMVC’ 19] |[pdf] | [official code - pytorch]
  • [Cascade RetinaNet] Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection | [BMVC’ 19] |[pdf]
  • Soft Sampling for Robust Object Detection | [BMVC’ 19] |[pdf]
  • Multi-adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV’ 19] |[pdf]
  • Towards Adversarially Robust Object Detection | [ICCV’ 19] |[pdf]
  • A Robust Learning Approach to Domain Adaptive Object Detection | [ICCV’ 19] |[pdf]
  • A Delay Metric for Video Object Detection: What Average Precision Fails to Tell | [ICCV’ 19] |[pdf]
  • Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach | [ICCV’ 19] |[pdf]
  • Employing Deep Part-Object Relationships for Salient Object Detection | [ICCV’ 19] |[pdf]
  • Learning Rich Features at High-Speed for Single-Shot Object Detection | [ICCV’ 19] |[pdf]
  • Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection | [ICCV’ 19] |[pdf]
  • Selectivity or Invariance: Boundary-Aware Salient Object Detection | [ICCV’ 19] |[pdf]
  • Progressive Sparse Local Attention for Video Object Detection | [ICCV’ 19] |[pdf]
  • Minimum Delay Object Detection From Video | [ICCV’ 19] |[pdf]
  • Towards Interpretable Object Detection by Unfolding Latent Structures | [ICCV’ 19] |[pdf]
  • Scaling Object Detection by Transferring Classification Weights | [ICCV’ 19] |[pdf]
  • [TridentNet] Scale-Aware Trident Networks for Object Detection | [ICCV’ 19] |[pdf]
  • Generative Modeling for Small-Data Object Detection | [ICCV’ 19] |[pdf]
  • Transductive Learning for Zero-Shot Object Detection | [ICCV’ 19] |[pdf]
  • Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection | [ICCV’ 19] |[pdf]
  • [CenterNet] CenterNet: Keypoint Triplets for Object Detection | [ICCV’ 19] |[pdf]
  • [DAFS] Dynamic Anchor Feature Selection for Single-Shot Object Detection | [ICCV’ 19] |[pdf]
  • [Auto-FPN] Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification | [ICCV’ 19] |[pdf]
  • Multi-Adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV’ 19] |[pdf]
  • Object Guided External Memory Network for Video Object Detection | [ICCV’ 19] |[pdf]
  • [ThunderNet] ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices | [ICCV’ 19] |[pdf]
  • [RDN] Relation Distillation Networks for Video Object Detection | [ICCV’ 19] |[pdf]
  • [MMNet] Fast Object Detection in Compressed Video | [ICCV’ 19] |[pdf]
  • Towards High-Resolution Salient Object Detection | [ICCV’ 19] |[pdf]
  • [SCAN] Stacked Cross Refinement Network for Edge-Aware Salient Object Detection | [ICCV’ 19] |[official code] |[pdf]
  • Motion Guided Attention for Video Salient Object Detection | [ICCV’ 19] |[pdf]
  • Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | [ICCV’ 19] |[pdf]
  • Learning to Rank Proposals for Object Detection | [ICCV’ 19] |[pdf]
  • [WSOD2] WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection | [ICCV’ 19] |[pdf]
  • [ClusDet] Clustered Object Detection in Aerial Images | [ICCV’ 19] |[pdf]
  • Towards Precise End-to-End Weakly Supervised Object Detection Network | [ICCV’ 19] |[pdf]
  • Few-Shot Object Detection via Feature Reweighting | [ICCV’ 19] |[pdf]
  • [Objects365] Objects365: A Large-Scale, High-Quality Dataset for Object Detection | [ICCV’ 19] |[pdf]
  • [EGNet] EGNet: Edge Guidance Network for Salient Object Detection | [ICCV’ 19] |[pdf]
  • Optimizing the F-Measure for Threshold-Free Salient Object Detection | [ICCV’ 19] |[pdf]
  • Sequence Level Semantics Aggregation for Video Object Detection | [ICCV’ 19] |[pdf]
  • [NOTE-RCNN] NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection | [ICCV’ 19] |[pdf]
  • Enriched Feature Guided Refinement Network for Object Detection | [ICCV’ 19] |[pdf]
  • [POD] POD: Practical Object Detection With Scale-Sensitive Network | [ICCV’ 19] |[pdf]
  • [FCOS] FCOS: Fully Convolutional One-Stage Object Detection | [ICCV’ 19] |[pdf]
  • [RepPoints] RepPoints: Point Set Representation for Object Detection | [ICCV’ 19] |[pdf]
  • Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection | [ICCV’ 19] |[pdf]
  • Weakly Supervised Object Detection With Segmentation Collaboration | [ICCV’ 19] |[pdf]
  • Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection | [ICCV’ 19] |[pdf]
  • Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes | [ICCV’ 19] |[pdf]
  • [C-MIDN] C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection | [ICCV’ 19] |[pdf]
  • Meta-Learning to Detect Rare Objects | [ICCV’ 19] |[pdf]
  • [Cap2Det] Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection | [ICCV’ 19] |[pdf]
  • [Gaussian YOLOv3] Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | [ICCV’ 19] |[pdf] [official code - c\\]
  • [FreeAnchor] FreeAnchor: Learning to Match Anchors for Visual Object Detection | [NeurIPS’ 19] |[pdf]
  • Memory-oriented Decoder for Light Field Salient Object Detection | [NeurIPS’ 19] |[pdf]
  • One-Shot Object Detection with Co-Attention and Co-Excitation | [NeurIPS’ 19] |[pdf]
  • [DetNAS] DetNAS: Backbone Search for Object Detection | [NeurIPS’ 19] |[pdf]
  • Consistency-based Semi-supervised Learning for Object detection | [NeurIPS’ 19] |[pdf]
  • [NATS] Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection | [NeurIPS’ 19] |[pdf]
  • [AA] Learning Data Augmentation Strategies for Object Detection | [arXiv’ 19] |[pdf]
  • [EfficientDet] EfficientDet: Scalable and Efficient Object Detection | [arXiv’ 19] |[pdf]

2020论文及代码

  • [Spiking-YOLO] Spiking-YOLO: Spiking Neural Network for Real-time Object Detection | [AAAI’ 20] |[pdf\\]
  • Tell Me What They’re Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction | [AAAI’ 20] |[pdf\\]
  • [CBnet] Cbnet: A novel composite backbone network architecture for object detection | [AAAI’ 20] |[pdf\\]
  • [Distance-IoU Loss] Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | [AAAI’ 20] |[pdf\\]
  • Computation Reallocation for Object Detection | [ICLR’ 20] |[pdf\\]
  • [YOLOv4] YOLOv4: Optimal Speed and Accuracy of Object Detection | [arXiv’ 20] |[pdf\\]
  • Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector | [CVPR’ 20] |[pdf\\]
  • Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels | [CVPR’ 20] |[pdf\\]
  • Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | [CVPR’ 20] |[pdf\\]
  • Rethinking Classification and Localization for Object Detection | [CVPR’ 20] |[pdf\\]
  • Multiple Anchor Learning for Visual Object Detection | [CVPR’ 20] |[pdf\\]
  • [CentripetalNet] CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection | [CVPR’ 20] |[pdf\\]
  • Learning From Noisy Anchors for One-Stage Object Detection | [CVPR’ 20] |[pdf\\]
  • [EfficientDet] EfficientDet: Scalable and Efficient Object Detection | [CVPR’ 20] |[pdf\\]
  • Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax | [CVPR’ 20] |[pdf\\]
  • Dynamic Refinement Network for Oriented and Densely Packed Object Detection | [CVPR’ 20] |[pdf\\]
  • Noise-Aware Fully Webly Supervised Object Detection | [CVPR’ 20] |[pdf\\]
  • [Hit-Detector] Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | [CVPR’ 20] |[pdf\\]
  • [D2Det] D2Det: Towards High Quality Object Detection and Instance Segmentation | [CVPR’ 20] |[pdf\\]
  • Prime Sample Attention in Object Detection | [CVPR’ 20] |[pdf\\]
  • Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection | [CVPR’ 20] |[pdf\\]
  • Exploring Categorical Regularization for Domain Adaptive Object Detection | [CVPR’ 20] |[pdf\\]
  • [SP-NAS] SP-NAS: Serial-to-Parallel Backbone Search for Object Detection | [CVPR’ 20] |[pdf\\]
  • [NAS-FCOS] NAS-FCOS: Fast Neural Architecture Search for Object Detection | [CVPR’ 20] |[pdf\\]
  • [DR Loss] DR Loss: Improving Object Detection by Distributional Ranking | [CVPR’ 20] |[pdf\\]
  • Detection in Crowded Scenes: One Proposal, Multiple Predictions | [CVPR’ 20] |[pdf\\]
  • [AugFPN] AugFPN: Improving Multi-Scale Feature Learning for Object Detection | [CVPR’ 20] |[pdf\\]
  • Robust Object Detection Under Occlusion With Context-Aware CompositionalNets | [CVPR’ 20] |[pdf\\]
  • Cross-Domain Document Object Detection: Benchmark Suite and Method | [CVPR’ 20] |[pdf\\]
  • Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection | [CVPR’ 20] |[pdf\\]
  • [SLV] SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection | [CVPR’ 20] |[pdf\\]
  • [HAMBox] HAMBox: Delving Into Mining High-Quality Anchors on Face Detection | [CVPR’ 20] |[pdf\\]
  • [Context R-CNN] Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection | [CVPR’ 20] |[pdf\\]
  • Mixture Dense Regression for Object Detection and Human Pose Estimation | [CVPR’ 20] |[pdf\\]
  • Offset Bin Classification Network for Accurate Object Detection | [CVPR’ 20] |[pdf\\]
  • [NETNet] NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection | [CVPR’ 20] |[pdf\\]
  • Scale-Equalizing Pyramid Convolution for Object Detection | [CVPR’ 20] |[pdf\\]
  • Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians | [CVPR’ 20] |[pdf\\]
  • [MnasFPN] MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices | [CVPR’ 20] |[pdf\\]
  • Physically Realizable Adversarial Examples for LiDAR Object Detection | [CVPR’ 20] |[pdf\\]
  • Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation | [CVPR’ 20] |[pdf\\]
  • Incremental Few-Shot Object Detection | [CVPR’ 20] |[pdf\\]
  • Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection | [CVPR’ 20] |[pdf\\]
  • Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation | [CVPR’ 20] |[pdf\\]
  • Learning a Unified Sample Weighting Network for Object Detection | [CVPR’ 20] |[pdf\\]
  • Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization | [CVPR’ 20] |[pdf\\]
  • DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution | [arXiv’ 20] |[pdf\\]
  • [DETR] End-to-End Object Detection with Transformers | [ECCV’ 20] |[pdf\\]
  • Suppress and Balance: A Simple Gated Network for Salient Object Detection | [ECCV’ 20] |[code\\]
  • [BorderDet] BorderDet: Border Feature for Dense Object Detection | [ECCV’ 20] |[pdf\\]
  • Corner Proposal Network for Anchor-free, Two-stage Object Detection | [ECCV’ 20] |[pdf\\]
  • A General Toolbox for Understanding Errors in Object Detection | [ECCV’ 20] |[pdf\\]
  • [Chained-Tracker] Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking | [ECCV’ 20] |[pdf\\]
  • Side-Aware Boundary Localization for More Precise Object Detection | [ECCV’ 20] |[pdf\\]
  • [PIoU] PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments | [ECCV’ 20] |[pdf\\]
  • [AABO] AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling | [ECCV’ 20] |[pdf\\]
  • Highly Efficient Salient Object Detection with 100K Parameters | [ECCV’ 20] |[pdf\\]
  • [GeoGraph] GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end | [ECCV’ 20] |[pdf\\]
  • Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection| [ECCV’ 20] |[pdf\\]
  • Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection | [ECCV’ 20] |[pdf\\]
  • Arbitrary-Oriented Object Detection with Circular Smooth Label | [ECCV’ 20] |[pdf\\]
  • Soft Anchor-Point Object Detection | [ECCV’ 20] |[pdf\\]
  • Object Detection with a Unified Label Space from Multiple Datasets | [ECCV’ 20] |[pdf\\]
  • [MimicDet] MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection | [ECCV’ 20] |[pdf\\]
  • Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions | [ECCV’ 20] |[pdf\\]
  • [Dynamic R-CNN] Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | [ECCV’ 20] |[pdf\\]
  • [OS2D] OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features | [ECCV’ 20] |[pdf\\]
  • Multi-Scale Positive Sample Refinement for Few-Shot Object Detection | [ECCV’ 20] |[pdf\\]
  • Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild | [ECCV’ 20] |[pdf\\]
  • Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection | [ECCV’ 20] |[pdf\\]
  • Two-Stream Active Query Suggestion for Large-Scale Object Detection in Connectomics | [ECCV’ 20] |[pdf\\]
  • [FDTS] FDTS: Fast Diverse-Transformation Search for Object Detection and Beyond | [ECCV’ 20]
  • Dual refinement underwater object detection network | [ECCV’ 20] |[pdf\\]
  • [APRICOT] APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection | [ECCV’ 20] |[pdf\\]
  • Large Batch Optimization for Object Detection: Training COCO in 12 Minutes | [ECCV’ 20] |[pdf\\]
  • Hierarchical Context Embedding for Region-based Object Detection | [ECCV’ 20] |[pdf\\]
  • Pillar-based Object Detection for Autonomous Driving | [ECCV’ 20] |[pdf\\]
  • Dive Deeper Into Box for Object Detection | [ECCV’ 20] |[pdf\\]
  • Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN | [ECCV’ 20] |[pdf\\]
  • Probabilistic Anchor Assignment with IoU Prediction for Object Detection | [ECCV’ 20] |[pdf\\]
  • [HoughNet] HoughNet: Integrating near and long-range evidence for bottom-up object detection | [ECCV’ 20] |[pdf\\]
  • [LabelEnc] LabelEnc: A New Intermediate Supervision Method for Object Detection | [ECCV’ 20] |[pdf\\]
  • Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer | [ECCV’ 20] |[pdf\\]
  • On the Importance of Data Augmentation for Object Detection | [ECCV’ 20] |[pdf\\]
  • Adaptive Object Detection with Dual Multi-Label Prediction | [ECCV’ 20] |[pdf\\]
  • Quantum-soft QUBO Suppression for Accurate Object Detection | [ECCV’ 20] |[pdf\\]
  • Improving Object Detection with Selective Self-supervised Self-training | [ECCV’ 20] |[pdf\\]

CVPR 2021

参考：https://blog.csdn.net/amusi1994/article/details/118387612

CVPR 2022

BoxeR: Box-Attention for 2D and 3D Transformers

Paper: https://arxiv.org/abs/2111.13087

Code: https://github.com/kienduynguyen/BoxeR

中文解读：https://mp.weixin.qq.com/s/UnUJJBwcAsRgz6TnQf_b7w

DN-DETR: Accelerate DETR Training by Introducing Query DeNoising

Paper: https://arxiv.org/abs/2203.01305

Code: https://github.com/FengLi-ust/DN-DETR

中文解读: https://mp.weixin.qq.com/s/xdMfZ_L628Ru1d1iaMny0w

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