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K-shot N-way Object Detection
- K means the number of the objects for each class
- N means the number of classes for few shot detection
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DataSet Split
- Base ClassSet ๐ถ_๐ , Base Dataset ๐ท_๐ contains {(๐ฅ_๐, ๐ฆ_๐)} about abundant images and annotations
- Novel ClassSet ๐ถ_๐ , Novel Dataset ๐ท_๐ contains {(๐ฅ_๐, ๐ฆ_๐)} about few images and annotations
- ๐ถ_๐ โฉ ๐ถ_๐=โ , ๐ถ_๐ โช ๐ถ_๐=๐ถ_๐ก๐๐ก๐๐
- COCO (Base : Novel = 60:20)ใ PASCAL VOC (Base : Novel = 15:5)
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Training(Two phases)
- Training on Base dataset
- Fine-tuning on Novel and base dataset with few objects
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Method
- (AAAI 2018) LSTD: A Low-Shot Transfer Detector for Object Detection [Paper]
- (ICCV 2019) Few-shot Object Detection via Feature Reweighting [Paper] [Code]
- (ICCV 2019) Meta-Learning to Detect Rare Objects [Paper]
- (ICCV 2019) Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning [Paper] [Code]
- (CVPR 2020) RepMet: Representative-based metric learning for classification and few-shot object detection [Paper] [Code]
- (ICML 2020) Frustratingly Simple Few-Shot Object Detection [Paper] [Code]
- (ECCV 2020) Multi-Scale Positive Sample Refinement for Few-Shot Object Detection [Paper] [Code]
- (ECCV 2020) Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild [Paper] [Code]
- (CVPR 2020) Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector [Paper] [Code]
- (AAAI 2020) Context-Transformer: Tackling Object Confusion for Few-Shot Detection [Paper] [Code]
- (CVPR 2020) Incremental Few-Shot Object Detection [Paper]
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(arxiv) Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning [Paper] [Code]
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(CVPR 2021) Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection [Paper]
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(CVPR 2021) Few-Shot Object Detection via Classification Refinement and Distractor Retreatment [Paper]
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(CVPR 2021) Generalized Few-Shot Object Detection without Forgetting [Paper] [Code]
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(CVPR 2021) FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding [Paper] [Code]
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(CVPR 2021) Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection [Paper] [Code]
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(CVPR 2021) Hallucination Improves Few-Shot Object Detection [Paper] [Code]
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(ICCV 2021) DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection [Paper] [Code]
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(AAAI 2021) StarNet: towards Weakly Supervised Few-Shot Object Detection [Paper]
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(CVPR 2021)Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection [Paper] [Code]
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(ICCV 2021) Universal-Prototype Augmentation for Few-Shot Object Detection [Paper] [Code]
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(MM 2021) Dual-awareness Attention for Few-Shot Object Detection [Paper]
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(arxiv) Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment [Paper]
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(arxiv) Class-Incremental Few-Shot Object Detection [Paper]
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(arxiv) Dynamic Relevance Learning for Few-Shot Object Detection [Paper] [Code]
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(NeurIPS 2021) Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [Paper] [Code]
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(NeurIPS 2021 Workshop) Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection [Paper]
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(NeurIPS 2021) Few-Shot Object Detection via Association and DIscrimination [Paper] [Code]
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(CVPR 2021) Transformation invariant few- shot object detection [Paper]
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(ICCV 2021) Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks [Paper]
- (CVPR 2022) Label, Verify, Correct: A Simple Few Shot Object Detection Method [Paper]