A plug-and-play LSTM-based attention module for person re-identification

Zhengxin Zeng; Zhuqing Jiang; Aidong Men; Guodong Ju

doi:10.1117/12.2557241

3 January 2020 A plug-and-play LSTM-based attention module for person re-identification

Zhengxin Zeng, Zhuqing Jiang, Aidong Men, Guodong Ju

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730C (2020) https://doi.org/10.1117/12.2557241
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

Spatial attention mechanism is widely used to extract local feature in person re-identification. However, some existing multi-stage spatial attention structures lack flexibility and require complicated training process. In this paper, a plug-and-play LSTM-based Attention Module(LAM) is proposed to enhance flexibility of the multi-attention mechanism. First, we employ the single-stage multi-attention structure to replace the traditional multi-stage multi-attention structure. Our structure encapsulates multiple attention machines in single module and thus the module can be added to any backbone networks without any modification directly. Then, correlation is introduced to spatial attention machines through LSTM. Correlation between different attention machines preserves diversity of the local feature and exploit the capacity of multi-attention mechanism. Moreover, the LAM is added to the backbone network in the form of residual, which enables the LAM to be trained with the backbone network synchronously. Therefore, the training process is simplified effectively. Experiments on CUHK03, Market-1501 and DukeMTMC-ReID datasets demonstrate the advantage of the proposed method.

Citation Download Citation

Zhengxin Zeng, Zhuqing Jiang, Aidong Men, and Guodong Ju "A plug-and-play LSTM-based attention module for person re-identification", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730C (3 January 2020); https://doi.org/10.1117/12.2557241

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Convolution

Convolutional neural networks

Networks

Data modeling

Image processing

Cameras

Communication engineering

Show All Keywords

Keywords/Phrases

Search In:

Publication Years