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PushupBench: Your VLM is not good at counting pushups

2026-04-25 18:58:33
Shengzhi Li, Jiarun Chen, Karun Sharma, Jiaqi Su, Shichao Pei
arXiv_AI
         
Abstract

Large vision-language models (VLMs) can recognize \textit{what} happens in video but fail to count \textit{how many} times. We introduce \textbf{PushupBench}, 446 long-form clips (avg. 36.7s) for evaluating repetition counting. The best frontier model achieves 42.1\% exact accuracy; open-source 4B models score $\sim$6\%, matching supervised baselines. We show that accuracy alone misleads -- weaker models exploit the modal count rather than reason temporally. Fine-tuning on counting with 1k samples transfers to general video understanding: MVBench (+2.15), PerceptionTest (+1.88), TVBench (+4.54), suggesting counting is a proxy for broader temporal this http URL incorporated in \texttt{lmms-eval} (this https URL) and hosted on (this http URL)

Abstract (translated)

URL

https://arxiv.org/abs/2604.23407

PDF

https://arxiv.org/pdf/2604.23407.pdf

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