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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 throug","title":"AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models","url":"https://arxiv.org/abs/2607.02269","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.02269v1 Announce Type: cross \nAbstract: 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. 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