Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation
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| Format: | Preprint |
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2026
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| author | Cai, Yujia Li, Boxuan Xu, Chenghao Yan, Jiexi |
| author_facet | Cai, Yujia Li, Boxuan Xu, Chenghao Yan, Jiexi |
| contents | Query-based image retrieval (QBIR) requires retrieving relevant images given diverse and often stylistically heterogeneous queries, such as sketches, artworks, or low-resolution previews. While large-scale vision--language representation models (VLRMs) like CLIP offer strong zero-shot retrieval performance, they struggle with distribution shifts caused by unseen query styles. In this paper, we propose the Hypernetwork-driven Style-adaptive Retrieval (Hystar), a lightweight framework that dynamically adapts model weights to each query's style. Hystar employs a hypernetwork to generate singular-value perturbations ($ΔS$) for attention layers, enabling flexible per-input adaptation, while static singular-value offsets on MLP layers ensure cross-style stability. To better handle semantic confusions across styles, we design StyleNCE as part of Hystar, an optimal-transport-weighted contrastive loss that emphasizes hard cross-style negatives. Extensive experiments on multi-style retrieval and cross-style classification benchmarks demonstrate that Hystar consistently outperforms strong baselines, achieving state-of-the-art performance while being parameter-efficient and stable across styles. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_10009 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation Cai, Yujia Li, Boxuan Xu, Chenghao Yan, Jiexi Computer Vision and Pattern Recognition Query-based image retrieval (QBIR) requires retrieving relevant images given diverse and often stylistically heterogeneous queries, such as sketches, artworks, or low-resolution previews. While large-scale vision--language representation models (VLRMs) like CLIP offer strong zero-shot retrieval performance, they struggle with distribution shifts caused by unseen query styles. In this paper, we propose the Hypernetwork-driven Style-adaptive Retrieval (Hystar), a lightweight framework that dynamically adapts model weights to each query's style. Hystar employs a hypernetwork to generate singular-value perturbations ($ΔS$) for attention layers, enabling flexible per-input adaptation, while static singular-value offsets on MLP layers ensure cross-style stability. To better handle semantic confusions across styles, we design StyleNCE as part of Hystar, an optimal-transport-weighted contrastive loss that emphasizes hard cross-style negatives. Extensive experiments on multi-style retrieval and cross-style classification benchmarks demonstrate that Hystar consistently outperforms strong baselines, achieving state-of-the-art performance while being parameter-efficient and stable across styles. |
| title | Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.10009 |