A Dual Self-Attention mechanism for vehicle re-Identification

Abstract

Vehicle re-identification has attracted tremendous attention from computer vision communities for its extensive applications in intelligent transportation and public security, while the high inter-class similarity and the large intra-class difference between vehicles bring out great challenges for re-identification (re-ID). To tackle these challenges, we learn from the self-attention mechanism in Natural Language Processing and propose a dual self-attention module to learn different regional dependencies: static self-attention for selectively refining semantic features and dynamic self-attention (called cross-region attention) for enhancing the spatial awareness of local feature. The static self-attention refines attended pixels within the entire image and salient regions, while the cross-region attention creatively captures the position-related regional dependencies for pixels within the windscreen area. These attention modules capture long-range dependencies and relative position information between different pixels or regions for vehicle feature learning globally and locally, realizing an efficient vehicle feature embedding by concatenating these augmented features for vehicle re-ID. Extensive experiments demonstrate the effectiveness and promising performance of our approach against the state-of-the-arts.