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Alignment Enhancement Network for Fine-grained Visual Categorization

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Published:31 March 2021Publication History
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Abstract

Fine-grained visual categorization (FGVC) aims to automatically recognize objects from different sub-ordinate categories. Despite attracting considerable attention from both academia and industry, it remains a challenging task due to subtle visual differences among different classes. Cross-layer feature aggregation and cross-image pairwise learning become prevailing in improving the performance of FGVC by extracting discriminative class-specific features. However, they are still inefficient to fully use the cross-layer information based on the simple aggregation strategy, while existing pairwise learning methods also fail to explore long-range interactions between different images. To address these problems, we propose a novel Alignment Enhancement Network (AENet), including two-level alignments, Cross-layer Alignment (CLA) and Cross-image Alignment (CIA). The CLA module exploits the cross-layer relationship between low-level spatial information and high-level semantic information, which contributes to cross-layer feature aggregation to improve the capacity of feature representation for input images. The new CIA module is further introduced to produce the aligned feature map, which can enhance the relevant information as well as suppress the irrelevant information across the whole spatial region. Our method is based on an underlying assumption that the aligned feature map should be closer to the inputs of CIA when they belong to the same category. Accordingly, we establish Semantic Affinity Loss to supervise the feature alignment within each CIA block. Experimental results on four challenging datasets show that the proposed AENet achieves the state-of-the-art results over prior arts.

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    • Published in

      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 17, Issue 1s
      January 2021
      353 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3453990
      Issue’s Table of Contents

      Copyright © 2021 Association for Computing Machinery.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 31 March 2021
      • Revised: 1 December 2020
      • Accepted: 1 December 2020
      • Received: 1 April 2020
      Published in tomm Volume 17, Issue 1s

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