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PERSON RE-IDENTIFICATION USING VISUAL ATTENTION

Citation Author(s):
Hairong Qi
Submitted by:
Alireza Rahimpour
Last updated:
14 September 2017 - 4:12pm
Document Type:
Poster
Document Year:
2017
Event:
Presenters:
Dr. Hairong Qi
Paper Code:
2620
 

Despite recent attempts for solving the person re-identification problem, it remains a challenging task since a person’s appearance can vary significantly when large variations in view angle, human pose and illumination are involved. The concept of attention is one of the most interesting recent architectural innovations in neural networks. Inspired by that, in this paper we propose a novel approach based on using a gradient-based attention mechanism in deep convolution neural network for solving the person re-identification problem. Our model learns to focus selectively on parts of the input image for which the networks’ output is most sensitive to. Extensive comparative evaluations demonstrate that the proposed method outperforms state-of-the-art approaches, including both traditional and deep neural network-based methods on the challenging CUHK01 and CUHK03 datasets.

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