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Video-HOCA benchmark reveals Video-LLMs struggle with anomaly explanation

By PulseAugur Editorial · [1 sources] ·

A new diagnostic benchmark called Video-HOCA has been introduced to evaluate the physical anomaly reasoning capabilities of Video Large Language Models (Video-LLMs). This benchmark utilizes an Ontological-Causal taxonomy and comprises over 1,400 videos with more than 3,470 question-answer pairs, verified by humans. Initial testing on 20 Instruct-mode Video-LLMs revealed that while recognition tasks scored around 75-88%, explanation tasks (Task II) had macro-F1 scores mostly below 50%, indicating a significant gap between recognizing anomalies and explaining them. AI

IMPACT Highlights a key limitation in current Video-LLMs, suggesting a need for improved reasoning and explanation capabilities beyond simple recognition.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Video-HOCA benchmark reveals Video-LLMs struggle with anomaly explanation

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The cluster describes a new academic paper introducing a benchmark for evaluating AI models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Chang Liu, Yunfan Ye, Qingyang Zhou, Xichen Tan, Mengxuan Luo, Zhenyu Qiu, Wei Peng, Zhiping Cai ·

    Video-HOCA: A Diagnostic Benchmark for Physical Anomaly Reasoning in Video-LLMs

    arXiv:2602.19571v2 Announce Type: replace Abstract: We introduce Video-HOCA, a diagnostic benchmark for physical anomaly reasoning in videos. Video-HOCA uses an Ontological-Causal taxonomy to distinguish violations of an entity's own properties or capabilities from violations of …

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