Liu, Yang, Zhou, Lei, Zhang, Pencheng et al. (5 more authors) (2022) Where to Focus:Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification. In: Avidan, Shai, Brostow, Gabriel, Cissé, Moustapha, Farinella, Giovanni Maria and Hassner, Tal, (eds.) Proceedings ECCV 2022. Lecture Notes in Computer Science (LNCS) . Springer , Cham , pp. 57-73.
Abstract
Object categories are often grouped into a multi-granularity taxonomic hierarchy. Classifying objects at coarser-grained hierarchy requires global and common characteristics, while finer-grained hierarchy classification relies on local and discriminative features. Therefore, humans should also subconsciously focus on different object regions when classifying different hierarchies. This granularity-wise attention is confirmed by our collected human real-time gaze data on different hierarchy classifications. To leverage this mechanism, we propose a Cross-Hierarchical Region Feature (CHRF) learning framework. Specifically, we first design a region feature mining module that imitates humans to learn different granularity-wise attention regions with multi-grained classification tasks. To explore how human attention shifts from one hierarchy to another, we further present a cross-hierarchical orthogonal fusion module to enhance the region feature representation by blending the original feature and an orthogonal component extracted from adjacent hierarchies. Experiments on five hierarchical fine-grained datasets demonstrate the effectiveness of CHRF compared with the state-of-the-art methods. Ablation study and visualization results also consistently verify the advantages of our human attention-oriented modules. The code and dataset are available at https://github.com/visiondom/CHRF.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 19 Aug 2022 08:00 |
Last Modified: | 13 Jan 2025 00:12 |
Published Version: | https://doi.org/10.1007/978-3-031-20053-3_4 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (LNCS) |
Identification Number: | 10.1007/978-3-031-20053-3_4 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:190185 |
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Description: Where to Focus: Investigating Hierarchical Attention Relationship for Fine-Grained Visual Classification