SenBen: Sensitive Scene Graphs for Explainable Content Moderation
Content moderation systems classify images as safe or unsafe but lack spatial grounding and interpretability: they cannot explain what sensitive behavior was detected, who is involved, or where it occurs. We introduce the Sensitive Benchmark (SenBen), the first large-scale scene graph benchmark for sensitive content, comprising 13,999 frames from 157 movies annotated with Visual Genome-style scene graphs (25 object classes, 28 attributes including affective states such as pain, fear, aggression, and distress, 14 predicates) and 16 sensitivity tags across 5 categories. We distill a frontier VLM into a compact 241M student model using a multi-task recipe that addresses vocabulary imbalance ...
| Authors | Fatih Cagatay Akyon, Alptekin Temizel |
|---|---|
| Primary category | cs.CV |
| Published | 9 April 2026 |
| Last updated at source | 26 May 2026 |
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