Multi-Scale Deep Feature Fusion for Vehicle Re-Identification

Yiting Cheng, Chuanfa Zhang, Kangzheng Gu, Lizhe Qi, Zhongxue Gan, Wenqiang Zhang

Abstract: 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 conduct both multi-scale and multi-level features for precise vehicle re-id. Based on the backbone deep CNN, MS-Deep mainly consists of two modules: 1) Multi-Scale Fusion (MSF) Block which encapsulates combination of multi-scale streams as MSF feature; 2) Multi-Level Fusion (MLF) Block which fuses MSF features of multiple levels to build the final descriptor. Importantly, in MSF, Multi-Scale Attention (MSA) is introduced to dynamically emphasize important channels of each scale, and Level-Wise Attention(LWA) is utilized in MLF to determine the different weightings for each MSF feature of different levels. As a result, experiments show that our MSDeep outperforms state-of-the-art algorithms on challenging VeRi and VehicleID benchmarks in terms of abundant and hierarchical hyper-descriptors.