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Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification

Person re-identification (re-id) aims to match people across non-overlapping camera views in a public space. This is a challenging problem because the people captured in surveillance videos often wear similar clothing. Consequently, the differences in their appearance are typically subtle and only detectable at particular locations and scales. In this paper, we propose a deep re-id network (MuDeep) that is composed of two novel types of layers - a multi-scale deep learning layer, and a leader-based attention learning layer. Specifically, the former learns deep discriminative feature representations at different scales, while the latter utilizes the information from multiple scales to lead and determine the optimal weightings for each scale. The importance of different spatial locations for extracting discriminative features is learned explicitly via our leader-based attention learning layer. Extensive experiments are carried out to demonstrate that the proposed MuDeep outperforms the state-of-the-art on a number of benchmarks and has a better generalization ability under a domain generalization setting.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.42 No.2 February 2020

No. Panggil

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Penerbit

: .,

Deskripsi Fisik

p. 371 - 385

Bahasa

English

ISBN/ISSN

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Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

PERSON RE-IDENTIFICATION
DOMAIN GENERALIZATION
MULTI-SCALE DEEP LEARNING
SELF-ATTENTION

Info Detail Spesifik

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Pernyataan Tanggungjawab

Xuelin Qian, Yanwei Fu, Tao Xiang, Yu-Gang Jiang, Xiangyang Xue

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