SenBen: Sensitive Scene Graphs for Explainable Content Moderation
Fatih Cagatay Akyon, Alptekin Temizel

TL;DR
SenBen introduces a large-scale scene graph benchmark for sensitive content, enabling explainable content moderation with improved models that outperform existing systems in accuracy and efficiency.
Contribution
The paper presents the first large-scale sensitive scene graph benchmark and a compact, efficient model that enhances explainability and performance in content moderation.
Findings
SenBen benchmark contains 13,999 frames with detailed annotations.
The proposed model improves SenBen Recall by 6.4 percentage points.
The model achieves faster inference and lower GPU memory usage than competitors.
Abstract
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 in autoregressive scene graph generation through suffix-based object identity, Vocabulary-Aware Recall (VAR) Loss, and a decoupled Query2Label tag head with asymmetric loss, yielding a +6.4 percentage…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
