Few-shot Object Detection via Dual-domain Feature Fusion and Patch-level Attention

Few-shot object detection receives much attention with the ability to detect novel class objects using limited annotated data. The transfer learning-based solution becomes popular due to its simple training with good accuracy, however, it is still challenging to enrich the feature diversity during the training process. And fine-grained features are also insufficient for novel class detection. To deal with the problems, this paper proposes a novel few-shot object detection method based on dual-domain feature fusion and patch-level attention. Upon original base domain, an elementary domain with more category-agnostic features is superposed to construct a two-stream backbone, which benefits to enrich the feature diversity. To better integrate various features, a dual-domain feature fusion is designed, where the feature pairs with the same size are complementarily fused to extract more discriminative features. Moreover, a patch-wise feature refinement termed as patch-level attention is presented to mine internal relations among the patches, which enhances the adaptability to novel classes. In addition, a weighted classification loss is given to assist the fine-tuning of the classifier by combining extra features from FPN of the base training model. In this way, the few-shot detection quality to novel class objects is improved. Experiments on PASCAL VOC and MS COCO datasets verify the effectiveness of the method.