Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

Wei Li, Xiatian Zhu, Shaogang Gong

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2194-2200. https://doi.org/10.24963/ijcai.2017/305

Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).
Keywords:
Machine Learning: Deep Learning
Multidisciplinary Topics and Applications: Validation and Verification
Robotics and Vision: Robotics and Vision