aiqbench analysis
AnyGroundBench introduces a critical evaluation framework for Vision-Language Models by testing their ability to adapt to specialized domains like surgery and industry using limited data. The study reveals that while current models struggle with zero-shot generalization in these niches, fine-tuning offers the most viable path forward despite domain-specific performance gaps. This benchmark effectively shifts the research focus from static accuracy metrics to practical domain adaptation capabilities essential for real-world deployment.
AnyGroundBench: A Multi-Domain Adaptation Benchmark for Video Grounding in VLMs
arXiv:2607.02269v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have shown strong performance in Spatio-Temporal Video Grounding (STVG), yet they are still evaluated mostly in a zero-shot manner on general-purpose benchmarks of everyday scenes. This creates a critical disconnect from real-world applications in specialized domains, where models inevitably encounter rare visual or textual concepts. Since exhaustive pre-training across infinite data distributions is infeasible, the ability to adapt to novel domains with limited data 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, expert-annotated videos with established datasets, unifying them through dense, high-fidelity spatio-temporal annotations. Crucially, the benchmark provides dedicated limited training subsets, enabling systematic evaluation of domain adaptability under limited training data. We benchmark 23 state-of-the-art VLMs in the zero-shot setting and further evaluate five adaptation strategies, spanning training-free and fine-tuning-based approaches, on representative models, assessing their zero-shot generalization and adaptation capacity. Our results show that current VLMs remain far from practical performance in the zero-shot setting, that training-free adaptation produces highly variable effects depending on the model and domain, and that fine-tuning-based adaptation, though more effective, still falls short of real-world requirements, with gains varying markedly across domains. These findings expose fundamental limitations in current VLMs' spatio-temporal reasoning, pointing to concrete directions for future research.
Abstract:Vision-Language Models (VLMs) have shown strong performance in Spatio-Temporal Video Grounding (STVG), yet they are still evaluated mostly in a zero-shot manner on general-purpose benchmarks of everyday scenes. This creates a critical disconnect from real-world applications in specialized domains, where models inevitably encounter rare visual or textual concepts. Since exhaustive pre-training across infinite data distributions is infeasible, the ability to adapt to novel domains with limited data 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, expert-annotated videos with established datasets, unifying them through dense, high-fidelity spatio-temporal annotations. Crucially, the benchmark provides dedicated limited training subsets, enabling systematic evaluation of domain adaptability under limited training data. We benchmark 23 state-of-the-art VLMs in the zero-shot setting and further evaluate five adaptation strategies, spanning training-free and fine-tuning-based approaches, on representative models, assessing their zero-shot generalization and adaptation capacity. Our results show that current VLMs remain far from practical performance in the zero-shot setting, that training-free adaptation produces highly variable effects depending on the model and domain, and that fine-tuning-based adaptation, though more effective, still falls short of real-world requirements, with gains varying markedly across domains. These findings expose fundamental limitations in current VLMs' spatio-temporal reasoning, pointing to concrete directions for future research.
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
arXiv:2607.02269 [cs.CV]
(or arXiv:2607.02269v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.02269 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 331 words
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