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

DataSet Split

• Base ClassSet 𝐶_𝑏 , Base Dataset 𝐷_𝑏 contains {(𝑥_𝑖, 𝑦_𝑖)} about abundant images and annotations
• Novel ClassSet 𝐶_𝑛 , Novel Dataset 𝐷_𝑛 contains {(𝑥_𝑖, 𝑦_𝑖)} about few images and annotations
• 𝐶_𝑏 ∩ 𝐶_𝑛=∅ , 𝐶_𝑏 ∪ 𝐶_𝑛=𝐶_𝑡𝑜𝑡𝑎𝑙
• COCO (Base : Novel = 60:20)、 PASCAL VOC (Base : Novel = 15:5)

Training(Two phases)

• Training on Base dataset
• Fine-tuning on Novel and base dataset with few objects

Method

• Meta-Learning Based

• Transfer-Learning Based

(arxiv) Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning [Paper] [Code]

(CVPR 2021) Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection [Paper]

(CVPR 2021) Few-Shot Object Detection via Classification Refinement and Distractor Retreatment [Paper]

(CVPR 2021) Generalized Few-Shot Object Detection without Forgetting [Paper] [Code]

(CVPR 2021) FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding [Paper] [Code]

(CVPR 2021) Dense Relation Distillation with Context-aware Aggregation for Few-Shot Object Detection [Paper] [Code]

(CVPR 2021) Hallucination Improves Few-Shot Object Detection [Paper] [Code]

(ICCV 2021) DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection [Paper] [Code]

(AAAI 2021) StarNet: towards Weakly Supervised Few-Shot Object Detection [Paper]

(CVPR 2021)Beyond Max-Margin: Class Margin Equilibrium for Few-shot Object Detection [Paper] [Code]

(ICCV 2021) Universal-Prototype Augmentation for Few-Shot Object Detection [Paper] [Code]

(MM 2021) Dual-awareness Attention for Few-Shot Object Detection [Paper]

(arxiv) Meta Faster R-CNN: Towards Accurate Few-Shot Object Detection with Attentive Feature Alignment [Paper]

(arxiv) Class-Incremental Few-Shot Object Detection [Paper]

(arxiv) Dynamic Relevance Learning for Few-Shot Object Detection [Paper] [Code]

(NeurIPS 2021) Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection [Paper] [Code]

(NeurIPS 2021 Workshop) Meta Guided Metric Learner for Overcoming Class Confusion in Few-Shot Road Object Detection [Paper]

(NeurIPS 2021) Few-Shot Object Detection via Association and DIscrimination [Paper] [Code]

(CVPR 2021) Transformation invariant few- shot object detection [Paper]

(ICCV 2021) Query Adaptive Few-Shot Object Detection with Heterogeneous Graph Convolutional Networks [Paper]