FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis
As AI-powered compliance monitoring becomes increasingly important in public governance and industrial safety, the ability to provide verifiable evidence and traceable accountability signals is essential. However, existing video anomaly detection datasets focus on event-level binary classification, lacking the rule-driven, explainable analysis required for real-world compliance scenarios. We introduce FoodMonitor, a benchmark for explainable compliance analysis in commercial kitchen surveillance
Record details
Published: 23 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Privacy · Transparency
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (23 May 2026), “FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis,” evidence record 3823, https://ethics.ai/record/3823 (originally published by arXiv).
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