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目标检测论文总结

阅读量：

2014论文及代码

2015论文及代码

2016论文及代码

2017论文及代码

2018论文及代码

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

2020

复制代码 
* **[Spiking-YOLO]** Spiking-YOLO: Spiking Neural Network for Real-time Object Detection | **[AAAI’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1903.06530.pdf)
* Tell Me What They’re Holding: Weakly-supervised Object Detection with Transferable Knowledge from Human-object Interaction | **[AAAI’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1911.08141v1.pdf)
* **[CBnet]** Cbnet: A novel composite backbone network architecture for object detection | **[AAAI’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1909.03625.pdf)
* **[Distance-IoU Loss]** Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression | **[AAAI’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1911.08287v1.pdf)
* Computation Reallocation for Object Detection | **[ICLR’ 20]** |[`[pdf\]`](https://openreview.net/pdf?id=SkxLFaNKwB)
* **[YOLOv4]** YOLOv4: Optimal Speed and Accuracy of Object Detection | **[arXiv’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2004.10934.pdf)
* Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1908.01998.pdf)
* Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2005.08455.pdf)
* 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)
* Rethinking Classification and Localization for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1904.06493.pdf)
* Multiple Anchor Learning for Visual Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.02252.pdf)
* **[CentripetalNet]** CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.09119.pdf)
* Learning From Noisy Anchors for One-Stage Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.05086.pdf)
* **[EfficientDet]** EfficientDet: Scalable and Efficient Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1911.09070.pdf)
* Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)
* Dynamic Refinement Network for Oriented and Densely Packed Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2005.09973.pdf)
* Noise-Aware Fully Webly Supervised Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Shen_Noise-Aware_Fully_Webly_Supervised_Object_Detection_CVPR_2020_paper.pdf)
* **[Hit-Detector]** Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.11818.pdf)
* **[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)
* Prime Sample Attention in Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1904.04821.pdf)
* Don’t Even Look Once: Synthesizing Features for Zero-Shot Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1911.07933.pdf)
* Exploring Categorical Regularization for Domain Adaptive Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.09152.pdf)
* **[SP-NAS]** SP-NAS: Serial-to-Parallel Backbone Search for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Jiang_SP-NAS_Serial-to-Parallel_Backbone_Search_for_Object_Detection_CVPR_2020_paper.pdf)
* **[NAS-FCOS]** NAS-FCOS: Fast Neural Architecture Search for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1906.04423.pdf)
* **[DR Loss]** DR Loss: Improving Object Detection by Distributional Ranking | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1907.10156.pdf)
* Detection in Crowded Scenes: One Proposal, Multiple Predictions | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.09163.pdf)
* **[AugFPN]** AugFPN: Improving Multi-Scale Feature Learning for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.05384.pdf)
* Robust Object Detection Under Occlusion With Context-Aware CompositionalNets | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Robust_Object_Detection_Under_Occlusion_With_Context-Aware_CompositionalNets_CVPR_2020_paper.pdf)
* Cross-Domain Document Object Detection: Benchmark Suite and Method | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.13197.pdf)
* Exploring Bottom-Up and Top-Down Cues With Attentive Learning for Webly Supervised Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.09790.pdf)
* **[SLV]** SLV: Spatial Likelihood Voting for Weakly Supervised Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_SLV_Spatial_Likelihood_Voting_for_Weakly_Supervised_Object_Detection_CVPR_2020_paper.pdf)
* **[HAMBox]** HAMBox: Delving Into Mining High-Quality Anchors on Face Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.09231.pdf)
* **[Context R-CNN]** Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.03538.pdf)
* Mixture Dense Regression for Object Detection and Human Pose Estimation | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.00821.pdf)
* Offset Bin Classification Network for Accurate Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Qiu_Offset_Bin_Classification_Network_for_Accurate_Object_Detection_CVPR_2020_paper.pdf)
* **[NETNet]** NETNet: Neighbor Erasing and Transferring Network for Better Single Shot Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2001.06690.pdf)
* Scale-Equalizing Pyramid Convolution for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2005.03101.pdf)
* Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians | **[CVPR’ 20]** |[`[pdf\]`](https://cse.buffalo.edu/~jsyuan/papers/2020/TFAN.pdf)
* **[MnasFPN]** MnasFPN: Learning Latency-Aware Pyramid Architecture for Object Detection on Mobile Devices | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.01106.pdf)
* Physically Realizable Adversarial Examples for LiDAR Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2004.00543.pdf)
* Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.10275.pdf)
* Incremental Few-Shot Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.04668.pdf)
* Where, What, Whether: Multi-Modal Learning Meets Pedestrian Detection | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Luo_Where_What_Whether_Multi-Modal_Learning_Meets_Pedestrian_Detection_CVPR_2020_paper.pdf)
* Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.11303.pdf)
* Learning a Unified Sample Weighting Network for Object Detection | **[CVPR’ 20]** |[`[pdf\]`](https://openaccess.thecvf.com/content_CVPR_2020/papers/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.pdf)
* Seeing without Looking: Contextual Rescoring of Object Detections for AP Maximization | **[CVPR’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.12290.pdf)
* DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution | **[arXiv’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2006.02334v1.pdf)
* **[DETR]** End-to-End Object Detection with Transformers | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2005.12872.pdf)
* Suppress and Balance: A Simple Gated Network for Salient Object Detection | **[ECCV’ 20]** |[`[code\]`](https://github.com/Xiaoqi-Zhao-DLUT/GateNet-RGB-Saliency)
* **[BorderDet]** BorderDet: Border Feature for Dense Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.11056.pdf)
* Corner Proposal Network for Anchor-free, Two-stage Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.13816.pdf)
* A General Toolbox for Understanding Errors in Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2008.08115v1.pdf)
* **[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)
* Side-Aware Boundary Localization for More Precise Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.04260.pdf)
* **[PIoU]** PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.09584.pdf)
* **[AABO]** AABO: Adaptive Anchor Box Optimization for Object Detection via Bayesian Sub-sampling | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.09336.pdf)
* Highly Efficient Salient Object Detection with 100K Parameters | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.05643.pdf)
* **[GeoGraph]** GeoGraph: Learning graph-based multi-view object detection with geometric cues end-to-end | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.10151.pdf)
* Many-shot from Low-shot: Learning to Annotate using Mixed Supervision for Object Detection| **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2008.09694.pdf)
* Cheaper Pre-training Lunch: An Efficient Paradigm for Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2004.12178.pdf)
* Arbitrary-Oriented Object Detection with Circular Smooth Label | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.05597.pdf)
* Soft Anchor-Point Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1911.12448.pdf)
* Object Detection with a Unified Label Space from Multiple Datasets | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2008.06614.pdf)
* **[MimicDet]** MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590528.pdf)
* Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.00070.pdf)
* **[Dynamic R-CNN]** Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2004.06002.pdf)
* **[OS2D]** OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.06800.pdf)
* Multi-Scale Positive Sample Refinement for Few-Shot Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.09384.pdf)
* Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.12107.pdf)
* Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2009.08119.pdf)
* Two-Stream Active Query Suggestion for Large-Scale Object Detection in Connectomics | **[ECCV’ 20]** |[`[pdf\]`](https://donglaiw.github.io/paper/2020_eccv_twostream.pdf)
* **[FDTS]** FDTS: Fast Diverse-Transformation Search for Object Detection and Beyond | **[ECCV’ 20]** * Dual refinement underwater object detection network | **[ECCV’ 20]** |[`[pdf\]`](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123650273.pdf)
* **[APRICOT]** APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/1912.08166.pdf)
* Large Batch Optimization for Object Detection: Training COCO in 12 Minutes | **[ECCV’ 20]** |[`[pdf\]`](http://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660477.pdf)
* Hierarchical Context Embedding for Region-based Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2008.01338.pdf)
* Pillar-based Object Detection for Autonomous Driving | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.10323.pdf)
* Dive Deeper Into Box for Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.14350.pdf)
* Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.01571.pdf)
* Probabilistic Anchor Assignment with IoU Prediction for Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.08103.pdf)
* **[HoughNet]** HoughNet: Integrating near and long-range evidence for bottom-up object detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.02355.pdf)
* **[LabelEnc]** LabelEnc: A New Intermediate Supervision Method for Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.03282.pdf)
* Boosting Weakly Supervised Object Detection with Progressive Knowledge Transfer | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.07986.pdf)
* On the Importance of Data Augmentation for Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/abs/1906.11172)
* Adaptive Object Detection with Dual Multi-Label Prediction | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2003.12943.pdf)
* Quantum-soft QUBO Suppression for Accurate Object Detection | **[ECCV’ 20]** |[`[pdf\]`](https://arxiv.org/pdf/2007.13992.pdf)
* Improving Object Detection with Selective Self-supervised Self-training | **[ECCV’ 20]** |[`[pdf\]`](

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