AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models
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-adap
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- PossiblePossibly related (embedding) · 55%vlm-starter →
- PossiblePossibly related (embedding) · 53%VioletVision-3B →
- PossiblePossibly related (embedding) · 50%open-edge-platform/geti →
- LinkedLinked via arxiv author · 85%Rintaro Otsubo →
“AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models”
- LinkedLinked via arxiv author · 85%Ryo Fujii →
“AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models”
- LinkedLinked via arxiv author · 85%Reina Ishikawa →
“AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models”
- LinkedLinked via arxiv author · 85%Taiki Kanaya →
“AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models”
- LinkedLinked via arxiv author · 85%Kanta Sawafuji →
“AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models”