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计算机视觉论文总结系列（一）：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
-
「[R-CNN]」 Rich feature hierarchies for accurate object detection and semantic segmentation 「[CVPR' 14]」 [pdf] [official code - caffe] -
「[OverFeat]」 OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | 「[ICLR' 14]」 |[pdf] [official code - torch] -
「[MultiBox]」 Scalable Object Detection using Deep Neural Networks | 「[CVPR' 14]」 |[pdf] -
「[SPP-Net]」 Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | 「[ECCV' 14]」 |[pdf] [official code - caffe] [unofficial code - keras] [unofficial code - tensorflow]
2015论文及代码
-
Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | 「[CVPR' 15]」 |[pdf] [official code - matlab] -
「[MR-CNN]」 Object detection via a multi-region & semantic segmentation-aware CNN model | 「[ICCV' 15]」 |[pdf] [official code - caffe\] -
「[DeepBox]」 DeepBox: Learning Objectness with Convolutional Networks | 「[ICCV' 15]」 |[pdf] [official code - caffe] -
「[AttentionNet]」 AttentionNet: Aggregating Weak Directions for Accurate Object Detection | 「[ICCV' 15]」 |[pdf] -
「[Fast R-CNN]」 Fast R-CNN | 「[ICCV' 15]」 |[pdf] [official code - caffe] -
「[DeepProposal]」 DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | 「[ICCV' 15]」 |[pdf] [official code - matconvnet] -
「[Faster R-CNN, RPN]」 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | 「[NIPS' 15]」 |[pdf] [official code - caffe] [unofficial code - tensorflow] [unofficial code - pytorch]
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
-
「[DSSD]」 DSSD : Deconvolutional Single Shot Detector | 「[arXiv' 17]」 |[pdf] [official code - caffe] -
「[TDM]」 Beyond Skip Connections: Top-Down Modulation for Object Detection | 「[CVPR' 17]」 |[pdf] -
「[FPN]」 Feature Pyramid Networks for Object Detection | 「[CVPR' 17]」 |[pdf] [unofficial code - caffe] -
「[YOLO v2]」 YOLO9000: Better, Faster, Stronger | 「[CVPR' 17]」 |[pdf] [official code - c] [unofficial code - caffe] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch] -
「[RON]」 RON: Reverse Connection with Objectness Prior Networks for Object Detection | 「[CVPR' 17]」 |[pdf] [official code - caffe] [unofficial code - tensorflow] -
「[RSA]」 Recurrent Scale Approximation for Object Detection in CNN | | 「[ICCV' 17]」 | [pdf\][official code - caffe] -
「[DCN]」 Deformable Convolutional Networks | 「[ICCV' 17]」 | [pdf\][official code - mxnet] [unofficial code - tensorflow] [unofficial code - pytorch] -
「[DeNet]」 DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | 「[ICCV' 17]」 |[pdf] [official code - theano] -
「[CoupleNet]」 CoupleNet: Coupling Global Structure with Local Parts for Object Detection | 「[ICCV' 17]」 |[pdf] [official code - caffe] -
「[RetinaNet]」 Focal Loss for Dense Object Detection | 「[ICCV' 17]」 |[pdf] [official code - keras] [unofficial code - pytorch] [unofficial code - mxnet] [unofficial code - tensorflow] -
「[Mask R-CNN]」 Mask R-CNN | 「[ICCV' 17]」 |[pdf] [official code - caffe2] [unofficial code - tensorflow] [unofficial code - tensorflow] [unofficial code - pytorch] -
「[DSOD]」 DSOD: Learning Deeply Supervised Object Detectors from Scratch | 「[ICCV' 17]」 |[pdf] [official code - caffe] [unofficial code - pytorch] -
「[SMN]」 Spatial Memory for Context Reasoning in Object Detection | 「[ICCV' 17]」 |[pdf] -
「[Light-Head R-CNN]」 Light-Head R-CNN: In Defense of Two-Stage Object Detector | 「[arXiv' 17]」 |[pdf] [official code - tensorflow] -
「[Soft-NMS]」 Improving Object Detection With One Line of Code | 「[ICCV' 17]」 |[pdf] [official code - caffe]
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
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「[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
「Accelerating DETR Convergence via Semantic-Aligned Matching」
Paper: https://arxiv.org/abs/2203.06883
Code: https://github.com/ZhangGongjie/SAM-DETR
「Localization Distillation for Dense Object Detection」
Paper: https://arxiv.org/abs/2102.12252
Code: https://github.com/HikariTJU/LD
Code2: https://github.com/HikariTJU/LD
中文解读：https://mp.weixin.qq.com/s/dxss8RjJH283h6IbPCT9vg
「Focal and Global Knowledge Distillation for Detectors」
Paper: https://arxiv.org/abs/2111.11837
Code: https://github.com/yzd-v/FGD
中文解读：https://mp.weixin.qq.com/s/yDkreTudC8JL2V2ETsADwQ
「A Dual Weighting Label Assignment Scheme for Object Detection」
Paper: https://arxiv.org/abs/2203.09730
Code: https://github.com/strongwolf/DW
「AdaMixer: A Fast-Converging Query-Based Object Detector」
Paper(Oral): https://arxiv.org/abs/2203.16507
Code: https://github.com/MCG-NJU/AdaMixer
「Omni-DETR: Omni-Supervised Object Detection with Transformers」
Paper: https://arxiv.org/abs/2203.16089
Code: https://github.com/amazon-research/omni-detr
「SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection」
Paper(Oral): https://arxiv.org/abs/2203.06398
Code: https://github.com/CityU-AIM-Group/SIGMA
CVPR 2023
「YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors」
Paper: https://arxiv.org/abs/2207.02696
Code: https://github.com/WongKinYiu/yolov7
「DETRs with Hybrid Matching」
Paper: https://arxiv.org/abs/2207.13080
Code: https://github.com/HDETR
「Enhanced Training of Query-Based Object Detection via Selective Query Recollection」
Paper: https://arxiv.org/abs/2212.07593
Code: https://github.com/Fangyi-Chen/SQR
「Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection」
Paper: https://arxiv.org/abs/2303.05892
Code: https://github.com/LutingWang/OADP
「本文参考：」
-
https://github.com/hoya012/deep_learning_object_detection -
https://arxiv.org/pdf/1809.02165v1.pdf -
https://gitcode.net/mirrors/amusi/CVPR2022-Papers-with-Code
作者介绍
GoAI
V1