Adaptive division and priori reinforcement part learning network for vehicle re-identification

Abstract

Vehicle Re-identification (Re-ID) recognizes images belonging to the same vehicle from a large number of vehicle images captured by different cameras. Learning subtle discriminative information in parts is key to meeting the challenge of small interclass difference in vehicle Re-ID. Methods that use additional models and annotations can accurately locate parts to learn part-level features, however, they require more computational and labor costs. The rigid division strategy can fully utilize the priori information to learn interpretable part features, but it breaks semantic continuity of parts and makes the interference of noise larger. In this paper, we propose an adaptive division part learning module (ADP). It adaptively generates spatially nonoverlapping diversity part masks based on multi-head self-attention semantic aggregation process to decouple part learning. It lets each head focus on the semantic aggregation of different parts and does not need to resort to additional annotations or models. In addition, we propose a priori reinforcement parts learning module (PRP). PRP establishes links between one part and all parts obtained by rigid division through a self-attention mechanism. This process emphasizes important detail information within the part from a global viewpoint and suppresses noise interference. Finally, based on the above two modules, we construct an adaptive division and priori reinforcement part learning network (ADPRP-Net) to learn granular features in an adaptive and priori way to deal with the challenge of small interclass difference. Experimental results on the VeRi-776 and VehicleID datasets show that ADPRP-Net achieves excellent vehicle Re-ID performance. And on the small test subset of the VehicleID dataset, ADPRP-Net has 3.3% higher Rank-1 accuracy and 1.7% higher Rank-5 accuracy compared to the state-of-the-art (SOTA) transformer-based Re-ID method (DSN). Code is available at https://github.com/zxy1116/ADPRP-Net.