AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection

Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce annotated examples. Existing methods solve this problem by performing subtasks of classification and localization utilizing a shared component in the detector, yet few of them take the distinct preferences towards feature embedding of two subtasks into consideration. In this paper, we carefully analyze the characteristics of FSOD, and present that a few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations. To the end, we propose a simple yet effective Adaptive Fully-Dual Network (AFD-Net). Specifically, we extend Faster R-CNN by introducing Dual Query Encoder and Dual Attention Generator for separate feature extraction, and Dual Aggregator for separate model reweighting. In this way, separate state estimation is achieved by the R-CNN detector. Furthermore, we introduce Adaptive Fusion Mechanism to guide the design of encoders for efficient feature fusion in the specific subtask. Extensive experiments on PASCAL VOC and MS COCO show that our approach achieves state-of-the-art performance by a large margin, demonstrating its effectiveness and generalization ability.