Dynamic Latent Feature Guidance for Few-Shot Object Detection

Few-shot object detection usually faces the challenge of imbalanced data distribution. The limited training data for novel classes not only leads to insufficient representation of support features but also biases the detector toward base classes. To address these problems, we propose a novel dynamic latent feature guidance method. First, the latent feature reconstruction module utilizes a variational autoencoder to reconstruct query and support features, extracting additional information representations from the latent space, thereby enriching feature representation and compensating for the information deficiencies caused by limited samples. Second, we design the dynamic multiscale similarity guidance module, which highlights information relevant to query images and suppresses background noise and occlusion interference through global, regional, and local similarities. Extensive experimental results demonstrate that our proposed method significantly improves detection accuracy on the PASCAL VOC and MS COCO datasets, outperforming existing state-of-the-art methods.