research-article

Beyond the Parts: Learning Coarse-to-Fine Adaptive Alignment Representation for Person Search

Authors:

Zheng WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 3

Article No.: 105, Pages 1 - 19

https://doi.org/10.1145/3565886

Published: 25 February 2023 Publication History

Abstract

Person search is a time-consuming computer vision task that entails locating and recognizing query people in scenic pictures. Body components are commonly mismatched during matching due to position variation, occlusions, and partially absent body parts, resulting in unsatisfactory person search results. Existing approaches for extracting local characteristics of the human body using keypoint information are unable to handle the search job when distinct body parts are misaligned, ignoring to exploit multiple granularities, which is crucial in the person search process. Moreover, the alignment learning methods learn body part features with fixed and equal weights, ignoring the beneficial contextual information, e.g., the umbrella carried by the pedestrian, which supplements compelling clues for identifying the person. In this paper, we propose a Coarse-to-Fine Adaptive Alignment Representation (CFA²R) network for learning multiple granular features in misaligned person search in the coarse-to-fine perspective. To exploit more beneficial body parts and related context of the cropped pedestrians, we design a Part-Attentional Progressive Module (PAPM) to guide the network to focus on informative body parts and positive accessorial regions. Besides, we propose a Re-weighting Alignment Module (RAM) shedding light on more contributive parts instead of treating them equally. Specifically, adaptive re-weighted but not fixed part features are reconstructed by Re-weighting Reconstruction module, considering that different parts serve unequally during image matching. Extensive experiments conducted on CUHK-SYSU and PRW datasets demonstrate competitive performance of our proposed method.

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Index Terms

Beyond the Parts: Learning Coarse-to-Fine Adaptive Alignment Representation for Person Search
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object identification
      2. Computer vision representations

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 19, Issue 3

May 2023

514 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3582886

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 25 February 2023

Online AM: 08 October 2022

Accepted: 26 September 2022

Revised: 30 August 2022

Received: 29 December 2021

Published in TOMM Volume 19, Issue 3

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Department of Science and Technology, Hubei Provincial People’s Government
National Natural Science Foundation of China

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https://dl.acm.org/doi/10.1145/3665247
Yang JWang ZHuang BAi JYang YXiong Z(2024)Joint Distortion Restoration and Quality Feature Learning for No-reference Image Quality AssessmentACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364989920:7(1-20)Online publication date: 27-Mar-2024
https://dl.acm.org/doi/10.1145/3649899
Zhang YDing DMa ZLi Z(2024)A Reconfigurable Framework for Neural Network Based Video In-Loop FilteringACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364046720:6(1-20)Online publication date: 8-Mar-2024
https://dl.acm.org/doi/10.1145/3640467
Huang BWang ZYang JHan ZLiang C(2024)Unlabeled Data Assistant: Improving Mask Robustness for Face RecognitionIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.335410919(3109-3123)Online publication date: 15-Jan-2024
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