Shaping Visual Representations with Attributes for Few-Shot Recognition
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Abstract (arXiv)
Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attribute information, into representation learning, which enhances the feature discrimination and improves the recognition performance. Most of these existing methods only consider the attribute information of support set while ignoring the query set, resulting in a potential loss of performance. In this letter, we propose a novel attribute-shaped learning (ASL) framework, which can jointly perform query attributes generation and discriminative visual representation learning for few-shot recognition. Specifically, a visual-attribute predictor (VAP) is constructed to predict the attributes of queries. By leveraging the attributes information, an attribute-visual attention module (AVAM) is designed, which can adaptively utilize attributes and visual representations to learn more discriminative features. Under the guidance of attribute modality, our method can learn enhanced semantic-aware representation for classification. Experiments demonstrate that our method can achieve competitive results on CUB and SUN benchmarks. Our source code is available at: urlhttps://github.com/chenhaoxing/ASL.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 13 Dec 2021.
Builds on
23 of 42 references.
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11 more
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.