CountVideoBench: A Benchmark to Evaluate Prediction Biases on the Object Counting task by Video-Language Models

arXiv:2610.28356v2 Announce Type: cross Abstract: Object counting in videos remains a poorly characterized capability of multimodal large language models. Counting questions are included in many existing VideoQA benchmarks, but the ground-truth count distributions in these benchmarks are heavily skewed toward small values. Across the nine benchmarks we survey, the median ground-truth count is at most five, and the three most frequent values together account for between 45% and 93% of the videos. The resulting mean accuracy is then dominated by the most frequent counts; a value-dependent bias a

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