Augmentative contrastive learning for one-shot object detection

Abstract

We present an Augmentative Contrastive Learning for One-Shot Object Detection method that is inspired by the co-attention and co-excitation (CoAE) method. In the One-shot Object Detection (OSOD) task, the target image contains rich background information that influences the outcomes of different categories of objects more significantly. As a result, the network frequently suggests objects that do not fall under this category, leading to false detections. To learn the similarity between the target object and the query patch, we built the data augmentation module of object switch by similarity, encourage and restrain module. It primarily consists of the following three components: 1) To improve the contrast between the foreground and background, we propose an object switch module based on data augmentation and feature similarity; 2) To decrease the likelihood of false detections, we propose an encourage and restrain module that allows the network to encourage the recommendation of similar objects while restricting the recommendation of different categories of objects; and 3) To increase classification results, we use the nonlinear projector. We perform tests on the PASCAL VOC and MS COCO datasets to confirm the efficacy of our technique.