Joint Co-Attention And Co-Reconstruction Representation Learning For One-Shot Object Detection

One-shot object detection aims to detect all candidate instances in a target image whose label class is unavailable in training, and only one labeled query image is given in testing. Nevertheless, insufficient utilization of the only known sample is one significant reason causing the performance degradation of current one-shot object detection models. To tackle the problem, we develop joint co-attention and co-reconstruction (CoAR) representation learning for one-shot object detection. The main contributions are described as follows. First, we propose a high-order feature fusion operation to exploit the deep co-attention of each target-query pair, which aims to enhance the correlation of the same class. Second, we use a low-rank structure to reconstruct the target-query feature in channel level, which aims to remove the irrelevant noise and enhance the latent similarity between the region proposals in target image and the query image. Experiments on both PASCAL VOC and MS COCO datasets demonstrate that our method outperforms previous state-of-the-art algorithms.