research-article

Double Attention Based on Graph Attention Network for Image Multi-Label Classification

Authors:

Haifeng HuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 19, Issue 1

Article No.: 18, Pages 1 - 23

https://doi.org/10.1145/3519030

Published: 05 January 2023 Publication History

Abstract

The task of image multi-label classification is to accurately recognize multiple objects in an input image. Most of the recent works need to leverage the label co-occurrence matrix counted from training data to construct the graph structure, which are inflexible and may degrade model generalizability. In addition, these methods fail to capture the semantic correlation between the channel feature maps to further improve model performance. To address these issues, we propose DA-GAT (a Double Attention framework based on the Graph Attention neTwork) to effectively learn the correlation between labels from training data. First, we devise a new channel attention mechanism to enhance the semantic correlation between channel feature maps, so as to implicitly capture the correlation between labels. Second, we propose a new label attention mechanism to avoid the adverse impact of a manually constructed label co-occurrence matrix. It only needs to leverage the label embedding as the input of network, then automatically constructs the label relation matrix to explicitly establish the correlation between labels. Finally, we effectively fuse the output of these two attention mechanisms to further improve model performance. Extensive experiments are conducted on three public multi-label classification benchmarks. Our DA-GAT model achieves mean average precision of 87.1%, 96.6%, and 64.3% on MS-COCO 2014, PASCAL VOC 2007, and NUS-WIDE, respectively, and obviously outperforms other existing state-of-the-art methods. In addition, visual analysis experiments demonstrate that each attention mechanism can capture the correlation between labels well and significantly promote the model performance.

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Index Terms

Double Attention Based on Graph Attention Network for Image Multi-Label Classification
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
2. Networks
  1. Network architectures

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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 1

January 2023

505 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3572858

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

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Publication History

Published: 05 January 2023

Online AM: 12 March 2022

Accepted: 14 February 2022

Revised: 29 January 2022

Received: 04 September 2021

Published in TOMM Volume 19, Issue 1

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https://dl.acm.org/doi/10.1145/3664653
Li ZWang RZhu FHan JHu SGurrin CKongkachandra RSchoeffmann KDang-Nguyen DRossetto LSatoh SZhou L(2024)Pyramidal Cross-Modal Transformer with Sustained Visual Guidance for Multi-Label Image ClassificationProceedings of the 2024 International Conference on Multimedia Retrieval10.1145/3652583.3658005(740-748)Online publication date: 30-May-2024
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