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s-Adaptive Decoupled Prototype for Few-Shot Object Detection

Meta-learning-based few-shot detectors use one K-average-pooled prototype (averaging along K-shot dimension) in both Region Proposal Network (RPN) and Detection head (DH) for query detection. Such plain operation would harm the FSOD performance in two aspects: 1) the poor quality of the prototype, and 2) the equivocal guidance due to the contradictions between RPN and DH. In this paper, we look closely into those critical issues and propose the s-Adaptive Decoupled Prototype (s-ADP) as a solution. To generate the high-quality prototype, we prioritize salient representations and deemphasize trivial variations by accessing both angle distance and magnitude dispersion (s) across K-support samples. To provide precise information for the query image, the prototype is decoupled into task-specific ones, which provide tailored guidance for 'where to look' and 'what to look for', respectively. Beyond that, we find our s-ADP can gradually strengthen the generalization power of encoding network during meta-training. So it can robustly deal with intra-class variations and a simple K- average pooling is enough to generate a high-quality prototype at meta-testing. We provide theoretical analysis to support its rationality. Extensive experiments on Pascal VOC, MS-COCO and FSOD datasets demonstrate that the proposed method achieves new state-of-the-art performance. Notably, our method surpasses the baseline model by a large margin - up to around 5.0% AP50 and 8.0% AP75 on novel classes.

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