Multi-Scale Deep Feature Fusion For Vehicle Re-Identification

Vehicle re-identification (re-id) is challenging due to the small inter-class distance. The differences between similar vehicles can be extremely subtle and only captured at particular scales and semantic levels. In this paper, we propose a novel Multi-Scale Deep Feature Fusion Network (MSDeep) to utilize both multi-scale and multi-level features and exploit their importance for precise vehicle re-id. MSDeep mainly consists of two modules: 1) Multi-Scale Fusion (MSF) Block which aggregates multi-scale combination as MSF feature; 2) Multi-Level Fusion (MLF) Block which fuses MSF features of multiple levels to build the final descriptor. Importantly, Multi-Scale Attention (MSA) of MSF and Level-Wise Attention (LWA) of MLF are introduced to dynamically emphasize discriminative scales and levels for different vehicles. As a result, MSDeep conducts abundant and hierarchical hyper-descriptors and outperforms state-of-the-art algorithms on challenging VeRi and VehicleID benchmarks.