Few-shot object detection based on multi-scale attention model

Conventional deep learning based object detection methods demand substantial annotated data for training, incurring considerable time and labor costs. Conversely, few-shot object detection necessitates only limited data from novel categories, emerging as a prominent research focus. This study proposes the Attention Contrastive Network (ACNet) to address few-shot object detection challenges. ACNet incorporates an attention mechanism architecture, extracting attention values and keys from image features in both support and query sets. It compares key attention across the sets and weights query set features with attention to augment local features. Additionally, multi-scale pooling layers enhance the network's capability to identify objects across varying scales. The introduction of an attract-repel mechanism in the loss function significantly amplifies inter-class differences, thereby improving classification accuracy. ACNet's efficacy is experimentally affirmed on the PASCAL VOC and COCO datasets, yielding commendable results in few-shot detection tasks.