└── README.md


/README.md:
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 1 | # Awesome Few-Shot Object Detection [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
 2 | 
 3 | A (not complete) list of few-shot object detection methods for computer vision.
 4 | While most methods include both, novel and base classes only a subset evaluates the performance separately for base and novel classes. The evaluation on base classes shows whether the method suffers from catastrophic forgetting or not.
 5 | 
 6 | |  Title  |Without Forgetting| Few-Shot Training | Venue |
 7 | |:--------|:--------|:--------|:--------|
 8 | |[Generalized Few-Shot Object Detection without Forgetting](https://openaccess.thecvf.com/content/CVPR2021/papers/Fan_Generalized_Few-Shot_Object_Detection_Without_Forgetting_CVPR_2021_paper.pdf)|Yes|Fine-tuning|CVPR'21|
 9 | |[Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Beyond_Max-Margin_Class_Margin_Equilibrium_for_Few-Shot_Object_Detection_CVPR_2021_paper.pdf)|No|Meta-training|CVPR'21|
10 | |[Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Hu_Dense_Relation_Distillation_With_Context-Aware_Aggregation_for_Few-Shot_Object_Detection_CVPR_2021_paper.pdf)|No|Meta-training|CVPR'21|
11 | |[Transformation Invariant Few-Shot Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Transformation_Invariant_Few-Shot_Object_Detection_CVPR_2021_paper.pdf)|No|Meta-training|CVPR'21|
12 | |[Few-Shot Object Detection via Classification Refinement and Distractor Retreatment](https://openaccess.thecvf.com/content/CVPR2021/papers/Li_Few-Shot_Object_Detection_via_Classification_Refinement_and_Distractor_Retreatment_CVPR_2021_paper.pdf)|No|Meta-training|CVPR'21|
13 | |[FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding](https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_FSCE_Few-Shot_Object_Detection_via_Contrastive_Proposal_Encoding_CVPR_2021_paper.pdf)|Yes|Fine-tuning|CVPR'21|
14 | |[Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhu_Semantic_Relation_Reasoning_for_Shot-Stable_Few-Shot_Object_Detection_CVPR_2021_paper.pdf)|Yes|Fine-tuning|CVPR'21|
15 | |[Accurate Few-shot Object Detection with Support-Query Mutual Guidance and Hybrid Loss](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Accurate_Few-Shot_Object_Detection_With_Support-Query_Mutual_Guidance_and_Hybrid_CVPR_2021_paper.pdf)|No|Meta-training|CVPR'21|
16 | |[Hallucination Improves Few-Shot Object Detection](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Hallucination_Improves_Few-Shot_Object_Detection_CVPR_2021_paper.pdf)|Yes|Fine-tuning|CVPR'21|
17 | |[Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment](https://arxiv.org/pdf/2104.07719.pdf)|Yes|Meta-training|ArXiv'21|
18 | |[Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning](https://arxiv.org/pdf/2103.11731v2.pdf)|Yes|Meta-training|ArXiv'21|
19 | |[Multi-Scale Positive Sample Refinement for Few-Shot Object Detection](https://arxiv.org/pdf/2007.09384.pdf)|Yes|Fine-tuning|ECCV'20|
20 | |[Incremental Few-Shot Object Detection](https://openaccess.thecvf.com/content_CVPR_2020/papers/Perez-Rua_Incremental_Few-Shot_Object_Detection_CVPR_2020_paper.pdf)|Yes|Meta-training|CVPR'20|
21 | |[Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_Few-Shot_Object_Detection_With_Attention-RPN_and_Multi-Relation_Detector_CVPR_2020_paper.pdf)|No|Meta-training|CVPR'20|
22 | |[Frustratingly Simple Few-Shot Object Detection](http://proceedings.mlr.press/v119/wang20j/wang20j.pdf)|Yes|Fine-tuning|ICML'20|
23 | |[Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning](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)|Yes|Meta-training|ICCV'19|
24 | 
25 | 
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