Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918462822023168 |
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| author | Zhang, Bingqing Cao, Zhuo Du, Heming Li, Yang Li, Xue Liu, Jiajun Wang, Sen |
| author_facet | Zhang, Bingqing Cao, Zhuo Du, Heming Li, Yang Li, Xue Liu, Jiajun Wang, Sen |
| contents | Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world query shifts, where the distribution of query data deviates from the training domain, leading to a sharp performance drop. Existing image-focused robustness solutions are inadequate to handle this vulnerability in video, as they fail to address the complex spatio-temporal dynamics inherent in these shifts. To systematically evaluate this vulnerability, we first introduce a comprehensive benchmark featuring 12 distinct types of video perturbations across five severity degrees. Analysis on this benchmark reveals that query shifts amplify the hubness phenomenon, where a few gallery items become dominant "hubs" that attract a disproportionate number of queries. To mitigate this, we then propose HAT-VTR (Hubness Alleviation for Test-time Video-Text Retrieval), as our baseline test-time adaptation framework designed to directly counteract hubness in VTR. It leverages two key components: a Hubness Suppression Memory to refine similarity scores, and multi-granular losses to enforce temporal feature consistency. Extensive experiments demonstrate that HAT-VTR substantially improves robustness, consistently outperforming prior methods across diverse query shift scenarios, and enhancing model reliability for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_20851 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts Zhang, Bingqing Cao, Zhuo Du, Heming Li, Yang Li, Xue Liu, Jiajun Wang, Sen Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition 68T05 I.2.10; H.3.3 Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world query shifts, where the distribution of query data deviates from the training domain, leading to a sharp performance drop. Existing image-focused robustness solutions are inadequate to handle this vulnerability in video, as they fail to address the complex spatio-temporal dynamics inherent in these shifts. To systematically evaluate this vulnerability, we first introduce a comprehensive benchmark featuring 12 distinct types of video perturbations across five severity degrees. Analysis on this benchmark reveals that query shifts amplify the hubness phenomenon, where a few gallery items become dominant "hubs" that attract a disproportionate number of queries. To mitigate this, we then propose HAT-VTR (Hubness Alleviation for Test-time Video-Text Retrieval), as our baseline test-time adaptation framework designed to directly counteract hubness in VTR. It leverages two key components: a Hubness Suppression Memory to refine similarity scores, and multi-granular losses to enforce temporal feature consistency. Extensive experiments demonstrate that HAT-VTR substantially improves robustness, consistently outperforming prior methods across diverse query shift scenarios, and enhancing model reliability for real-world applications. |
| title | Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts |
| topic | Information Retrieval Artificial Intelligence Computer Vision and Pattern Recognition 68T05 I.2.10; H.3.3 |
| url | https://arxiv.org/abs/2604.20851 |