{"ID":5675417,"CreatedAt":"2026-07-03T01:40:09.565152011Z","UpdatedAt":"2026-07-09T06:48:03.043455024Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02269","arxiv_id":"2607.02269","title":"AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models","abstract":"Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are largely confined to zero-shot assessments on general, daily-life benchmarks. This creates a critical disconnect from real-world applications in specialized fields, where models inevitably encounter rare visual concepts and complex spatio-temporal dynamics. Since exhaustive pre-training across infinite data distributions is infeasible, the ability to adapt to novel domains is essential. To bridge this gap, we introduce AnyGroundBench, a domain-adaptation benchmark designed to shift the STVG evaluation paradigm from static zero-shot testing to rigorous domain adaptation. Targeting five specialized domains (animal, industry, sports, surgery, and public security), AnyGroundBench pairs newly captured videos such as expert-annotated mouse behaviors with established datasets, unifying them through dense, high-fidelity spatio-temporal annotations. Crucially, the benchmark provides dedicated training subsets to systematically measure domain adaptability. We extensively evaluate 15 state-of-the-art VLMs, assessing their zero-shot generalization and In-Context Learning (ICL) capabilities under practical computational constraints. Ultimately, our findings reveal that current models fail in both zero-shot and ICL-based adaptation when confronted with specialized domains, exposing critical flaws in spatio-temporal reasoning that future research must address.","short_abstract":"Vision-Language Models (VLMs) have demonstrated immense promise in Spatio-Temporal Video Grounding (STVG). However, current evaluation protocols are largely confined to zero-shot assessments on general, daily-life benchmarks. This creates a critical disconnect from real-world applications in specialized fields, where m...","url_abs":"https://arxiv.org/abs/2607.02269","url_pdf":"https://arxiv.org/pdf/2607.02269v1","authors":"[\"Rintaro Otsubo\",\"Ryo Fujii\",\"Reina Ishikawa\",\"Taiki Kanaya\",\"Kanta Sawafuji\",\"Hiroki Kajita\",\"Shigeki Sakai\",\"Hideo Saito\",\"Ryo Hachiuma\"]","published":"2026-07-02T14:52:45Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Language Model\"]","has_code":false}