SCAN: Stronger Collaborative Attention Network for Vehicle Re-Identification

Abstract

Extracting robust feature representation is one of the key challenges in person/vehicle re-identification (ReID). Although approaches based on convolutional neural networks (CNN) for local feature learning have been very successful in person ReID, they have not been discussed deeply about effective ways to obtain local features for slicing. The horizontal slicing is commonly used for person ReID, which is more likely to cause fine-grained information to be overlooked in vehicle ReID with diverse orientations and a discriminative information distribution different from that of the person. To overcome these limitations, we propose the stronger collaborative attention network (SCAN). Specifically, we first perform cross slicing of backbone features in each scale branches to ensure that different fine-grained information can be divided into different parts with higher probability, and fuse different directional features of the same order of parts in each local scale branch to gradually strengthen the global nature of local vehicle features by associating other regions with common regions of different directions. To further mine the useful information, we design three modules carefully. (i) The collaborative feature refinement module (CFR) is proposed to further explore the collaborative relationship of local features and filter out the redundancy of collaborative information in local scale branches by superposition fusion strategy. (ii) The discriminative edge information learning module (DEL) is proposed to learn reliable discriminative edge information by adding edge parts in each local scale branch. (iii) The nonvisual information embedding module (NIE) is introduced to mitigate the feature bias caused by background and vehicle shape by embedding learnable explicit nonvisual camera and view information. Experimental results of our proposed method are superior, which achieve state-of-the-art performance on vehicle ReID benchmarks.