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| 序號 | 縮寫 | 文章題目 | 期刊 | 鏈接 |
|---|---|---|---|---|
| 1 | R-CNN | Rich feature hierarchies for accurate object detection and semantic segmentation | CVPR’ 14 | |
| 2 | OverFeat | OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | ICLR’ 14 | |
| 3 | MultiBox | Scalable Object Detection using Deep Neural Networks | CVPR’ 14 | |
| 4 | SPP-Net | Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | ECCV’ 14 | |
| 5 | Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | CVPR’ 15 | ||
| 6 | MR-CNN | Object detection via a multi-region & semantic segmentation-aware CNN model | ICCV’ 15 | |
| 7 | DeepBox | DeepBox: Learning Objectness with Convolutional Networks | ICCV’ 15 | |
| 8 | AttentionNet | AttentionNet: Aggregating Weak Directions for Accurate Object Detection | ICCV’ 15 | |
| 9 | Fast R-CNN | Fast R-CNN | ICCV’ 15 | |
| 10 | DeepProposal | DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | ICCV’ 15 | |
| 11 | Faster R-CNN, RPN | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | NIPS’ 15 | |
| 12 | YOLO v1 | You Only Look Once: Unified, Real-Time Object Detection | CVPR’ 16 | |
| 13 | G-CNN | G-CNN: an Iterative Grid Based Object Detector | CVPR’ 16 | |
| 14 | AZNet | Adaptive Object Detection Using Adjacency and Zoom Prediction | CVPR’ 16 | |
| 15 | ION | Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | CVPR’ 16 | |
| 16 | HyperNet | HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | CVPR’ 16 | |
| 17 | OHEM | Training Region-based Object Detectors with Online Hard Example Mining | CVPR’ 16 | |
| 18 | CRAPF | CRAFT Objects from Images | CVPR’ 16 | |
| 19 | MPN | A MultiPath Network for Object Detection | BMVC’ 16 | |
| 20 | SSD | SSD: Single Shot MultiBox Detector | ECCV’ 16 | |
| 21 | GBDNet | Crafting GBD-Net for Object Detection | ECCV’ 16 | |
| 22 | CPF | Contextual Priming and Feedback for Faster R-CNN | ECCV’ 16 | |
| 23 | MS-CNN | A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | ECCV’ 16 | |
| 24 | R-FCN | R-FCN: Object Detection via Region-based Fully Convolutional Networks | NIPS’ 16 | |
| 25 | PVANET | PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | NIPSW’ 16 | |
| 26 | DeepID-Net | DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | PAMI’ 16 | |
| 27 | NoC | Object Detection Networks on Convolutional Feature Maps | TPAMI’ 16 | |
| 28 | DSSD | DSSD : Deconvolutional Single Shot Detector | arXiv’ 17 | |
| 29 | TDM | Beyond Skip Connections: Top-Down Modulation for Object Detection | CVPR’ 17 | |
| 30 | FPN | Feature Pyramid Networks for Object Detection | CVPR’ 17 | |
| 31 | YOLO v2 | YOLO9000: Better, Faster, Stronger | CVPR’ 17 | |
| 32 | RON | RON: Reverse Connection with Objectness Prior Networks for Object Detection | CVPR’ 17 | |
| 33 | RSA | Recurrent Scale Approximation for Object Detection in CNN | ICCV’ 17 | |
| 34 | DCN | Deformable Convolutional Networks | ICCV’ 17 | |
| 35 | DeNet | DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | ICCV’ 17 | |
| 36 | CoupleNet | CoupleNet: Coupling Global Structure with Local Parts for Object Detection | ICCV’ 17 | |
| 37 | RetinaNet | Focal Loss for Dense Object Detection | ICCV’ 17 | |
| 38 | Mask R-CNN | Mask R-CNN | ICCV’ 17 | |
| 39 | DSOD | DSOD: Learning Deeply Supervised Object Detectors from Scratch | ICCV’ 17 | |
| 40 | SMN | Spatial Memory for Context Reasoning in Object Detection | ICCV’ 17 | |
| 41 | Light-Head R-CNN | Light-Head R-CNN: In Defense of Two-Stage Object Detector | arXiv’ 17 | |
| 42 | Soft-NMS | Improving Object Detection With One Line of Code | ICCV’ 17 | |
| 43 | YOLO v3 | YOLOv3: An Incremental Improvement | arXiv’ 18 | |
| 44 | ZIP | Zoom Out-and-In Network with Recursive Training for Object Proposal | IJCV’ 18 | |
| 45 | SIN | Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | CVPR’ 18 | |
| 46 | STDN | Scale-Transferrable Object Detection | CVPR’ 18 | |
| 47 | RefineDet | Single-Shot Refinement Neural Network for Object Detection | CVPR’ 18 | |
| 48 | MegDet | MegDet: A Large Mini-Batch Object Detector | CVPR’ 18 | |
| 49 | DA Faster R-CNN | Domain Adaptive Faster R-CNN for Object Detection in the Wild | CVPR’ 18 | |
| 50 | SNIP | An Analysis of Scale Invariance in Object Detection – SNIP | CVPR’ 18 | |
| 51 | Relation-Network | Relation Networks for Object Detection | CVPR’ 18 | |
| 52 | Cascade R-CNN | Cascade R-CNN: Delving into High Quality Object Detection | CVPR’ 18 | |
| 53 | Finding Tiny Faces in the Wild with Generative Adversarial Network | CVPR’ 18 | ||
| 54 | MLKP | Multi-scale Location-aware Kernel Representation for Object Detection | CVPR’ 18 | |
| 55 | Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | CVPR’ 18 | ||
| 56 | Fitness NMS | Improving Object Localization with Fitness NMS and Bounded IoU Loss | CVPR’ 18 | |
| 57 | STDnet | STDnet: A ConvNet for Small Target Detection | BMVC’ 18 | |
| 58 | RFBNet | Receptive Field Block Net for Accurate and Fast Object Detection | ECCV’ 18 | |
| 59 | Zero-Annotation Object Detection with Web Knowledge Transfer | ECCV’ 18 | ||
| 60 | CornerNet | CornerNet: Detecting Objects as Paired Keypoints | ECCV’ 18 | |
| 61 | PFPNet | Parallel Feature Pyramid Network for Object Detection | ECCV’ 18 | |
| 62 | Softer-NMS | Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | arXiv’ 18 | |
| 63 | ShapeShifter | ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | ECML-PKDD’ 18 | |
| 64 | Pelee | Pelee: A Real-Time Object Detection System on Mobile Devices | NIPS’ 18 | |
| 65 | HKRM | Hybrid Knowledge Routed Modules for Large-scale Object Detection | NIPS’ 18 | |
| 66 | MetaAnchor | MetaAnchor: Learning to Detect Objects with Customized Anchors | NIPS’ 18 | |
| 67 | SNIPER | SNIPER: Efficient Multi-Scale Training | NIPS’ 18 | |
| 68 | M2Det | M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | AAAI’ 19 | |
| 69 | R-DAD | Object Detection based on Region Decomposition and Assembly | AAAI’ 19 | |
| 70 | CAMOU | CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | ICLR’ 19 | |
| 71 | Feature Intertwiner for Object Detection | ICLR’ 19 | ||
| 72 | GIoU | Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | CVPR’ 19 | |
| 73 | Automatic adaptation of object detectors to new domains using self-training | CVPR’ 19 | ||
| 74 | Libra R-CNN | Libra R-CNN: Balanced Learning for Object Detection | CVPR’ 19 | |
| 75 | FSAF | Feature Selective Anchor-Free Module for Single-Shot Object Detection | CVPR’ 19 | |
| 76 | ExtremeNet | Bottom-up Object Detection by Grouping Extreme and Center Points | CVPR’ 19 | |
| 77 | C-MIL | C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection | CVPR’ 19 | |
| 78 | ScratchDet | ScratchDet: Training Single-Shot Object Detectors from Scratch | CVPR’ 19 | |
| 79 | Bounding Box Regression with Uncertainty for Accurate Object Detection | CVPR’ 19 | ||
| 80 | Activity Driven Weakly Supervised Object Detection | CVPR’ 19 | ||
| 81 | Towards Accurate One-Stage Object Detection with AP-Loss | CVPR’ 19 | ||
| 82 | Strong-Weak Distribution Alignment for Adaptive Object Detection | CVPR’ 19 | ||
| 83 | NAS-FPN | NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | CVPR’ 19 | |
| 84 | Adaptive NMS | Adaptive NMS: Refining Pedestrian Detection in a Crowd | CVPR’ 19 | |
| 85 | Point in, Box out: Beyond Counting Persons in Crowds | CVPR’ 19 | ||
| 86 | Locating Objects Without Bounding Boxes | CVPR’ 19 | ||
| 87 | Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | CVPR’ 19 | ||
| 88 | Towards Universal Object Detection by Domain Attention | CVPR’ 19 | ||
| 89 | Exploring the Bounds of the Utility of Context for Object Detection | CVPR’ 19 | ||
| 90 | What Object Should I Use? - Task Driven Object Detection | CVPR’ 19 | ||
| 91 | Dissimilarity Coefficient based Weakly Supervised Object Detection | CVPR’ 19 | ||
| 92 | Adapting Object Detectors via Selective Cross-Domain Alignment | CVPR’ 19 | ||
| 93 | Fully Quantized Network for Object Detection | CVPR’ 19 | ||
| 94 | Distilling Object Detectors with Fine-grained Feature Imitation | CVPR’ 19 | ||
| 95 | Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | CVPR’ 19 | ||
| 96 | Reasoning-RCNN | Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | CVPR’ 19 | |
| 97 | Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | CVPR’ 19 | ||
| 98 | Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | CVPR’ 19 | ||
| 99 | Spatial-aware Graph Relation Network for Large-scale Object Detection | CVPR’ 19 | ||
| 100 | MaxpoolNMS | MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | CVPR’ 19 | |
| 101 | You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | CVPR’ 19 | ||
| 102 | Object detection with location-aware deformable convolution and backward attention filtering | CVPR’ 19 | ||
| 103 | Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | CVPR’ 19 | ||
| 104 | GFR | Improving Object Detection from Scratch via Gated Feature Reuse | BMVC’ 19 | |
| 105 | Cascade RetinaNet | Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection | BMVC’ 19 | |
| 106 | Soft Sampling for Robust Object Detection | BMVC’ 19 | ||
| 107 | Multi-adversarial Faster-RCNN for Unrestricted Object Detection | ICCV’ 19 | ||
| 108 | Towards Adversarially Robust Object Detection | ICCV’ 19 | ||
| 109 | A Robust Learning Approach to Domain Adaptive Object Detection | ICCV’ 19 | ||
| 110 | A Delay Metric for Video Object Detection: What Average Precision Fails to Tell | ICCV’ 19 | ||
| 111 | Delving Into Robust Object Detection From Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach | ICCV’ 19 | ||
| 112 | Employing Deep Part-Object Relationships for Salient Object Detection | ICCV’ 19 | ||
| 113 | Learning Rich Features at High-Speed for Single-Shot Object Detection | ICCV’ 19 | ||
| 114 | Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection | ICCV’ 19 | ||
| 115 | Selectivity or Invariance: Boundary-Aware Salient Object Detection | ICCV’ 19 | ||
| 116 | Progressive Sparse Local Attention for Video Object Detection | ICCV’ 19 | ||
| 117 | Minimum Delay Object Detection From Video | ICCV’ 19 | ||
| 118 | Towards Interpretable Object Detection by Unfolding Latent Structures | ICCV’ 19 | ||
| 119 | Scaling Object Detection by Transferring Classification Weights | ICCV’ 19 | ||
| 120 | TridentNet | Scale-Aware Trident Networks for Object Detection | ICCV’ 19 | |
| 121 | Generative Modeling for Small-Data Object Detection | ICCV’ 19 | ||
| 122 | Transductive Learning for Zero-Shot Object Detection | ICCV’ 19 | ||
| 123 | Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection | ICCV’ 19 | ||
| 124 | CenterNet | CenterNet: Keypoint Triplets for Object Detection | ICCV’ 19 | |
| 125 | DAFS | Dynamic Anchor Feature Selection for Single-Shot Object Detection | ICCV’ 19 | |
| 126 | Auto-FPN | Auto-FPN: Automatic Network Architecture Adaptation for Object Detection Beyond Classification | ICCV’ 19 | |
| 127 | Multi-Adversarial Faster-RCNN for Unrestricted Object Detection | ICCV’ 19 | ||
| 128 | Object Guided External Memory Network for Video Object Detection | ICCV’ 19 | ||
| 129 | ThunderNet | ThunderNet: Towards Real-Time Generic Object Detection on Mobile Devices | ICCV’ 19 | |
| 130 | RDN | Relation Distillation Networks for Video Object Detection | ICCV’ 19 | |
| 131 | MMNet | Fast Object Detection in Compressed Video | ICCV’ 19 | |
| 132 | Towards High-Resolution Salient Object Detection | ICCV’ 19 | ||
| 133 | SCAN | Stacked Cross Refinement Network for Edge-Aware Salient Object Detection | ICCV’ 19 | |
| 134 | Motion Guided Attention for Video Salient Object Detection | ICCV’ 19 | ||
| 135 | Semi-Supervised Video Salient Object Detection Using Pseudo-Labels | ICCV’ 19 | ||
| 136 | Learning to Rank Proposals for Object Detection | ICCV’ 19 | ||
| 137 | WSOD2 | WSOD2: Learning Bottom-Up and Top-Down Objectness Distillation for Weakly-Supervised Object Detection | ICCV’ 19 | |
| 138 | ClusDet | Clustered Object Detection in Aerial Images | ICCV’ 19 | |
| 139 | Towards Precise End-to-End Weakly Supervised Object Detection Network | ICCV’ 19 | ||
| 140 | Few-Shot Object Detection via Feature Reweighting | ICCV’ 19 | ||
| 141 | Objects365 | Objects365: A Large-Scale, High-Quality Dataset for Object Detection | ICCV’ 19 | |
| 142 | EGNet | EGNet: Edge Guidance Network for Salient Object Detection | ICCV’ 19 | |
| 143 | Optimizing the F-Measure for Threshold-Free Salient Object Detection | ICCV’ 19 | ||
| 144 | Sequence Level Semantics Aggregation for Video Object Detection | ICCV’ 19 | ||
| 145 | NOTE-RCNN | NOTE-RCNN: NOise Tolerant Ensemble RCNN for Semi-Supervised Object Detection | ICCV’ 19 | |
| 146 | Enriched Feature Guided Refinement Network for Object Detection | ICCV’ 19 | ||
| 147 | POD | POD: Practical Object Detection With Scale-Sensitive Network | ICCV’ 19 | |
| 148 | FCOS | FCOS: Fully Convolutional One-Stage Object Detection | ICCV’ 19 | |
| 149 | RepPoints | RepPoints: Point Set Representation for Object Detection | ICCV’ 19 | |
| 150 | Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection | ICCV’ 19 | ||
| 151 | Weakly Supervised Object Detection With Segmentation Collaboration | ICCV’ 19 | ||
| 152 | Leveraging Long-Range Temporal Relationships Between Proposals for Video Object Detection | ICCV’ 19 | ||
| 153 | Detecting 11K Classes: Large Scale Object Detection Without Fine-Grained Bounding Boxes | ICCV’ 19 | ||
| 154 | C-MIDN | C-MIDN: Coupled Multiple Instance Detection Network With Segmentation Guidance for Weakly Supervised Object Detection | ICCV’ 19 | |
| 155 | Meta-Learning to Detect Rare Objects | ICCV’ 19 | ||
| 156 | Cap2Det | Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection | ICCV’ 19 | |
| 157 | Gaussian YOLOv3 | Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | ICCV’ 19 | |
| 158 | FreeAnchor | FreeAnchor: Learning to Match Anchors for Visual Object Detection | NeurIPS’ 19 | |
| 159 | Memory-oriented Decoder for Light Field Salient Object Detection | NeurIPS’ 19 | ||
| 160 | One-Shot Object Detection with Co-Attention and Co-Excitation | NeurIPS’ 19 | ||
| 161 | DetNAS | DetNAS: Backbone Search for Object Detection | NeurIPS’ 19 | |
| 162 | Consistency-based Semi-supervised Learning for Object detection | NeurIPS’ 19 | ||
| 163 | NATS | Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection | NeurIPS’ 19 | |
| 164 | AA | Learning Data Augmentation Strategies for Object Detection | arXiv’ 19 | |
| 165 | EfficientDet | EfficientDet: Scalable and Efficient Object Detection | arXiv’ 19 | |
| 166 | Spiking-YOLO | Spiking-YOLO: Spiking Neural Network for Real-time Object Detection | AAAI’ 20 | |
| 167 | Tell Me What They’re Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction | AAAI’ 20 | ||
| 168 | Tell Me What They’re Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction | AAAI’ 20 | ||
| 169 | Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | AAAI’ 20 | ||
| 170 | Computation Reallocation for Object Detection | ICLR’ 20 | ||
| 171 | Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection | CVPR’ 20 | ||
| 172 | Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector | CVPR’ 20 | ||
| 173 | AugFPN | AugFPN: Improving Multi-scale Feature Learning for Object Detection | CVPR’ 20 | |
| 174 | Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | CVPR’ 20 | ||
| 175 | Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation | CVPR’ 20 | ||
| 176 | CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection | CVPR’ 20 |