└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Papers-with-Codes of Few-Shot Object Detection 2 | 3 | 4 | ## 1. Few Shot Object Detection Preliminaries 5 | * K-shot N-way Object Detection 6 | - K means the number of the objects for each class 7 | - N means the number of classes for few shot detection 8 | * DataSet Split 9 | - Base ClassSet 𝐶_𝑏 , Base Dataset 𝐷_𝑏 contains {(𝑥_𝑖, 𝑦_𝑖)} about abundant images and annotations 10 | - Novel ClassSet 𝐶_𝑛 , Novel Dataset 𝐷_𝑛 contains {(𝑥_𝑖, 𝑦_𝑖)} about few images and annotations 11 | - 𝐶_𝑏 ∩ 𝐶_𝑛=∅ , 𝐶_𝑏 ∪ 𝐶_𝑛=𝐶_𝑡𝑜𝑡𝑎𝑙 12 | - COCO (Base : Novel = 60:20)、 PASCAL VOC (Base : Novel = 15:5) 13 | * Training(Two phases) 14 | - Training on Base dataset 15 | - Fine-tuning on Novel and base dataset with few objects 16 | 17 | * Method 18 | - Meta-Learning Based 19 | ![image](https://user-images.githubusercontent.com/47490392/131458670-89e7e75e-433a-4aea-8b23-a01ee85a1b54.png) 20 | 21 | - Transfer-Learning Based 22 | ![image](https://user-images.githubusercontent.com/47490392/131458759-824a1385-a276-4e99-be93-4d0d73cf5885.png) 23 | 24 | 25 | ## 2. Recent work 26 | ### 2018 27 | 28 | - (AAAI 2018) LSTD: A Low-Shot Transfer Detector for Object Detection [[Paper]](https://arxiv.org/pdf/1803.01529.pdf) 29 | 30 | ### 2019 31 | - (ICCV 2019) Few-shot Object Detection via Feature Reweighting [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Kang_Few-Shot_Object_Detection_via_Feature_Reweighting_ICCV_2019_paper.pdf) [[Code]](https://github.com/bingykang/Fewshot_Detection) 32 | - (ICCV 2019) Meta-Learning to Detect Rare Objects [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Meta-Learning_to_Detect_Rare_Objects_ICCV_2019_paper.pdf) 33 | - (ICCV 2019) Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning [[Paper]](https://openaccess.thecvf.com/content_ICCV_2019/papers/Yan_Meta_R-CNN_Towards_General_Solver_for_Instance-Level_Low-Shot_Learning_ICCV_2019_paper.pdf) [[Code]](https://github.com/yanxp/MetaR-CNN) 34 | - (CVPR 2020) RepMet: Representative-based metric learning for classification and few-shot object detection [[Paper]](https://openaccess.thecvf.com/content_CVPR_2019/papers/Karlinsky_RepMet_Representative-Based_Metric_Learning_for_Classification_and_Few-Shot_Object_Detection_CVPR_2019_paper.pdf) [[Code]](https://github.com/jshtok/RepMet) 35 | 36 | ### 2020 37 | - (ICML 2020) Frustratingly Simple Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2003.06957.pdf) [[Code]](https://github.com/ucbdrive/few-shot-object-detection) 38 | - (ECCV 2020) Multi-Scale Positive Sample Refinement for Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2007.09384.pdf) [[Code]](https://github.com/jiaxi-wu/MPSR) 39 | - (ECCV 2020) Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild [[Paper]](https://arxiv.org/pdf/2007.12107.pdf) [[Code]](https://github.com/YoungXIAO13/FewShotDetection) 40 | - (CVPR 2020) Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Few-Shot_Object_Detection_With_Attention-RPN_and_Multi-Relation_Detector_CVPR_2020_paper.pdf) [[Code]](https://github.com/fanq15/FewX) 41 | - (AAAI 2020) Context-Transformer: Tackling Object Confusion for Few-Shot Detection [[Paper]](https://arxiv.org/pdf/2003.07304.pdf) [[Code]](https://github.com/Ze-Yang/Context-Transformer) 42 | - (CVPR 2020) Incremental Few-Shot Object Detection [[Paper]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Perez-Rua_Incremental_Few-Shot_Object_Detection_CVPR_2020_paper.pdf) 43 | 44 | ### 2021 45 | - (arxiv) Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning [[Paper]](http://arxiv.org/abs/2103.11731) [[Code]](https://github.com/ZhangGongjie/Meta-DETR) 46 | - (CVPR 2021) Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.pdf) 47 | - (CVPR 2021) Few-Shot Object Detection via Classification Refinement and Distractor Retreatment [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.pdf) 48 | - (CVPR 2021) Generalized Few-Shot Object Detection without Forgetting [[Paper]](https://github.com/Megvii-BaseDetection/GFSD) [[Code]](https://arxiv.org/pdf/2105.09491.pdf) 49 | - (CVPR 2021) FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding [[Paper]](https://arxiv.org/pdf/2103.05950.pdf) [[Code]](https://github.com/MegviiDetection/FSCE) 50 | - (CVPR 2021) Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Dense_Relation_Distillation_With_Context-Aware_Aggregation_for_Few-Shot_Object_Detection_CVPR_2021_paper.pdf) [[Code]](https://github.com/hzhupku/DCNet) 51 | - (CVPR 2021) Hallucination Improves Few-Shot Object Detection [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Hallucination_Improves_Few-Shot_Object_Detection_CVPR_2021_paper.pdf) [[Code]](https://github.com/pppplin/HallucFsDet) 52 | - (ICCV 2021) DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2108.09017.pdf) [[Code]](https://github.com/er-muyue/DeFRCN) 53 | - (AAAI 2021) StarNet: towards Weakly Supervised Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2003.06798.pdf) 54 | - (CVPR 2021)Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Beyond_Max-Margin_Class_Margin_Equilibrium_for_Few-Shot_Object_Detection_CVPR_2021_paper.pdf) [[Code]](https://github.com/Bohao-Lee/CME) 55 | - (ICCV 2021) Universal-Prototype Augmentation for Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2103.01077.pdf) [[Code]](https://github.com/AmingWu/UP-FSOD) 56 | - (MM 2021) Dual-awareness Attention for Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2102.12152.pdf) 57 | - (arxiv) Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment [[Paper]](https://arxiv.org/pdf/2104.07719.pdf) 58 | - (arxiv) Class-Incremental Few-Shot Object Detection [[Paper]](https://arxiv.org/pdf/2105.07637.pdf) 59 | - (arxiv) Dynamic Relevance Learning for Few-Shot Object Detection [[Paper]](https://arxiv.org/ftp/arxiv/papers/2108/2108.02235.pdf) [[Code]](https://github.com/liuweijie19980216/DRL-for-FSOD) 60 | - (NeurIPS 2021) Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [[Paper]](https://arxiv.org/pdf/2110.15017v1.pdf) [[Code]](https://github.com/dongnana777/Bridging-Non-Co-occurrence) 61 | - (NeurIPS 2021 Workshop) Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection [[Paper]](https://arxiv.org/pdf/2110.15074v1.pdf) 62 | 63 | - (NeurIPS 2021) Few-Shot Object Detection via Association and DIscrimination [[Paper]](https://papers.nips.cc/paper/2021/file/8a1e808b55fde9455cb3d8857ed88389-Paper.pdf) [[Code]](https://github.com/yhcao6/FADI) 64 | - (CVPR 2021) Transformation invariant few- shot object detection [[Paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Transformation_Invariant_Few-Shot_Object_Detection_CVPR_2021_paper.pdf) 65 | - (ICCV 2021) Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks [[Paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Han_Query_Adaptive_Few-Shot_Object_Detection_With_Heterogeneous_Graph_Convolutional_Networks_ICCV_2021_paper.pdf) 66 | 67 | ### 2022 68 | - (CVPR 2022) Label, Verify, Correct: A Simple Few Shot Object Detection Method [[Paper]](https://arxiv.org/pdf/2112.05749.pdf) 69 | --------------------------------------------------------------------------------