Affiliations: [a] School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, China
| [b] School of Fundamental Sciences, Massey University, Palmerston North, New Zealand
| [c] Beijing Branch of China United Network Communication Co., Ltd, Beijing, China
Correspondence:
[*]
Corresponding author. Yingchun Guo, School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin, 300401, China. E-mail: [email protected].
Abstract: Person re-identification with natural language description is a process of retrieving the corresponding person’s image from an image dataset according to a text description of the person. The key challenge in this cross-modal task is to extract visual and text features and construct loss functions to achieve cross-modal matching between text and image. Firstly, we designed a two-branch network framework for person re-identification with natural language description. In this framework we include the following: a Bi-directional Long Short-Term Memory (Bi-LSTM) network is used to extract text features and a truncated attention mechanism is proposed to select the principal component of the text features; a MobileNet is used to extract image features. Secondly, we proposed a Cascade Loss Function (CLF), which includes cross-modal matching loss and single modal classification loss, both with relative entropy function, to fully exploit the identity-level information. The experimental results on the CUHK-PEDES dataset demonstrate that our method achieves better results in Top-5 and Top-10 than other current 10 state-of-the-art algorithms.
Keywords: Person re-identification, cross-modal, natural language description, cascade loss function, truncated attention mechanism
