Few-shot Object Detection Method Based on Query Guidance and Semantic Enhancement

This study proposes a Few-Shot Object Detection (FSOD) method based on a query-guided strategy and semantic enhancement mechanism to address the following concerns: the lack of prototypical key information, insufficient adaptation to query images in the meta-learning paradigm, and the detector's sensitivity to the variance of the novel class leading to misclassification. The Query Guidance Module (QGM) conditionally couples query-aware information into support features by learning the correlation between the query and support features, aiming to generate specific and representative prototypes for each query image. The Visual Semantic Enhancement Module (VSEM) distils the knowledge from textual semantic information that matches the novel class of visual features and adaptively enhances these features to improve their discriminability and mitigate variance sensitivity for better classification. In addition, the classification and regression tasks are decoupled, and semantic enhancement is performed on the classification branch to facilitate the model's understanding of the target semantics. The experimental results demonstrate that, compared to the currently known state-of-the-art SMPCCNet method, the proposed approach achieves an average improvement of 2.2 percentage points in novel Average Precision (nAP) on the PASCAL VOC dataset and an average improvement of 1.0 percentage points in Average Precision (AP) on the MS COCO dataset, validating its effectiveness.