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AnyGroundBench Benchmark Exposes Vision-Language Models' Domain-Adaptation WeaknessesAnyGroundBench基准测试揭示：视觉语言模型在专业领域适应中的关键缺陷
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- Researchers introduced AnyGroundBench, a domain-adaptation benchmark for evaluating video grounding in vision-language models across five specialized domains: animal, industry, sports, surgery, and public security.
- The benchmark evaluated 15 state-of-the-art VLMs on both zero-shot generalization and In-Context Learning capabilities, using newly captured expert-annotated videos paired with established datasets for dense spatio-temporal annotations.
- Findings reveal current models fail significantly 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.
- 研究人员推出AnyGroundBench基准测试，用于评估视觉语言模型在五个专业领域（动物、工业、运动、手术和公共安全）的视频定位能力。
- 该基准测试对15个先进的视觉语言模型进行了评估，测试其零样本泛化和上下文学习能力，使用新捕获的专家标注视频与现有数据集配对，进行密集的时空注释。
- 研究发现当前模型在零样本和基于ICL的领域适应中均表现失败，在专业领域中暴露出时空推理的关键缺陷，需要未来研究改进。
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AnyGroundBench: A Specialized-Domain Benchmark for Video Grounding in Vision-Language Models
2026-07-01
hf-daily-papers