One Shot Object Detection Via Hierarchical Adaptive Alignment

Abstract

Recently, deep learning based object detectors have achieved good performance with abundant labeled data. However, data labeling is often expensive and time-consuming in real life. Therefore, it is required to introduce one shot learning into object detection. In this paper, we propose one shot object detection based on hierarchical adaptive alignment to address the limited information of one shot in feature representation. We present a multi-adaptive alignment framework based on faster R-CNN to extract effective features from query patch and target image using siamese convolutional feature extraction, then generate a fused feature map by aggregating query and target features. We use the fused feature map in object classification and localization. The proposed framework adaptively adjusts feature representation through hierarchical and aggregated alignment so that it can learn correlation between the target image and the query patch. Experimental results demonstrate that the proposed method significantly improves the unseen-class object detection from 24.3 AP50 to 26.2 AP50 on the MS-COCO dataset.