Multi-attention deep reinforcement learning and re-ranking for vehicle re-identification
Date of Original Version
For solving the vehicle Re-identification (Re-ID) task, we need to focus our attention on the details with arbitrary size in the image, and it's tough to locate these details accurately. In this paper, we propose a Multi-Attention Deep Reinforcement Learning (MADRL) model to focus on multi-attentional subregions that spreading randomly in the image, and extract the discriminative features for the Re-ID task. First, we obtain multiple attentions from the representative features, then group the feature channels into different parts, then train a deep reinforcement learning model to learn more accurate positions of these fine-grained details with different losses. Unlike existing models with complex strategies to keep the patch-matching constrains, our MADRL model can automatically locate the matching patches (multi-attentional subregions) in different vehicle images with the same identification (ID). Furthermore, based on the fine-grained attention and global features we re-calculate the distance between the inter- and intra- classes, and we get better re-ranking results. Compared with state-of-the-art methods on three large-scale vehicle Re-ID datasets, our algorithm greatly improves the performance of vehicle Re-ID.
Publication Title, e.g., Journal
Liu, Yu, Jianbing Shen, and Haibo He. "Multi-attention deep reinforcement learning and re-ranking for vehicle re-identification." Neurocomputing 414, (2020): 27-35. doi: 10.1016/j.neucom.2020.07.020.