Transformer-Based Few-Shot Object Detection with Enhanced Fine-Tuning Stability

Few-shot object detection (FSOD) aims at detecting unseen classes with limited annotated novel examples. while the fine-tuning paradigm has been proven effective for FSOD, most existing approaches are based on Faster R-CNN framework, neglecting exploration of advanced frameworks such as DETR. In this paper, we propose a transformer-based few-shot object detection method. To address the instability issue in few-shot fine-tuning of DETR, we propose a simple yet effective method to enhance the fine-tuning stability from two perspectives: 1) an enhanced initialization method for classification layer, hollow initialization. 2) Category-Level Dropout (CLD), to mitigate effect of missing annotations by controlling gradient backpropagation of classifier. Comprehensive experimental results on MS COCO show that our method significantly improves the baseline and achieves very promising performance.