FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding

Gueter Josmy Faure; Min-Hung Chen; Jia-Fong Yeh; Hung-Ting Su; Winston H. Hsu

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arXiv · 2605.19846

FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding

Gueter Josmy Faure·Min-Hung Chen·Jia-Fong Yeh·Hung-Ting Su·Winston H. Hsu

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Abstract

Vision-Language Models (VLMs) have demonstrated remarkable capabilities in general video understanding, yet they often struggle with the fine-grained comprehension crucial for real-world applications requiring nuanced interpretation of human actions and interactions. While some recent human-centric benchmarks evaluate aspects of model behaviour such as fairness/ethics, emotion perception, and broader human-centric metrics, they do not combine long-form videos, very dense QA coverage, and frame-level spatial/temporal grounding at scale. To bridge this gap, we introduce FineBench, a human-centric video question answering (VQA) benchmark specifically designed to assess fine-grained understanding. FineBench comprises 199,420 multiple-choice QA pairs densely annotated across 64 long-form videos (15 minutes each), focusing on detailed person movement, person interaction, and object manipulation, including compositional actions. Our extensive evaluation reveals that while proprietary models like GPT-5 achieve respectable performance, current open-source VLMs significantly underperform, struggling particularly with spatial reasoning in multi-person scenes and distinguishing subtle differences in human movements and interactions. To address these identified weaknesses, we propose FineAgent, a modular framework that enhances VLMs by leveraging a Localizer and a Descriptor. Experiments show that FineAgent consistently improves the performance of various open VLMs on FineBench. FineBench provides a rigorous testbed for future research into fine-grained human-centric video understanding, while FineAgent offers a practical approach to enhance such reasoning in current VLMs. Project page and code at https://joslefaure.github.io/assets/html/finebench.html.

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Gueter Josmy Faure, Min-Hung Chen, Jia-Fong Yeh, Hung-Ting Su, Winston H. Hsu. 2026-05-19. FineBench: Benchmarking and Enhancing Vision-Language Models for Fine-grained Human Activity Understanding. https://arxiv.org/abs/2605.19846

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