Attribute Attention for Semantic Disambiguation in Zero-Shot Learning
Abstract
Zero-shot learning (ZSL) aims to accurately recognize unseen objects by learning mapping matrices that bridge the gap between visual information and semantic attributes. Previous works implicitly treat attributes equally in compatibility score while ignoring that they have different importance for discrimination, which leads to severe semantic ambiguity. Considering both low-level visual information and global class-level features that relate to this ambiguity, we propose a practical Latent Feature Guided Attribute Attention (LFGAA) framework to perform object-based attribute attention for semantic disambiguation. By distracting semantic activation in dimensions that cause ambiguity, our method outperforms existing state-of-the-art methods on AwA2, CUB and SUN datasets in both inductive and transductive settings.
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During zero-shot learning, additional visual and semantic features such as word embeddings, visual attributes, or descriptions can be assigned to both seen and unseen classes.
“However, during the learning process, additional visual and semantic features such as word embeddings [132], visual attributes [133], or descriptions [134] can be assigned to both seen and unseen classes.”
From A survey on deep multimodal learning for computer vision: advances, trends, applications, and datasets (2022) · cites this paper as [133]
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References 50 · Cited by 175
- Authors
- Liu, Y., Guo, J., Cai, D., & He, X.
- Affiliations
- Alibaba Group (China) (CN)
- Guangzhou Automobile Group (China) (CN)
- ORCID
- Venue
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 6697-6706
- OpenAlex
- W2991813857
- Cited by
- 175
- References
- 50
- Citations by year
- 2026: 5 · 2025: 18 · 2024: 32 · 2023: 34 · 2022: 32 · 2021: 40 · 2020: 13 · 2019: 1
- FWCI
- 8.23
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- zero-shot learning, semantic disambiguation, object recognition, attribute importance, cubs, sun
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