arXiv Machine Learning
Aug 31

What Do Interaction Representations Actually Measure? Pre-Event Separability in Weakly-Supervised Violence Detection

The paper investigates whether detailed articulated human pose provides more discriminative power than coarse spatial relationships for early violence detection. By fixing the downstream pipeline and comparing five interaction representations—including bounding‑box geometry, handcrafted pose analogues, enriched pose descriptors, and a learned joint encoder—the study finds that pose‑based representations do not outperform coarse geometry. When visual encoders are frozen and evaluated on larger datasets, person‑crop appearance and whole‑frame context outperform geometry, but cropping to interacting people offers no advantage over encoding the entire frame. The authors further demonstrate that pre‑onset frames contain source‑related artifacts (e.g., title cards, watermarks) that contribute significantly to discrimination, suggesting that benchmark performance may reflect these artifacts rather than true event evidence.

By Parishruthi Ganesh
arXiv AI
Aug 28

Multi2AV-Safety: Benchmarking Safety in Multimodal-to-Audio-Video Generation

Multi2AV‑Safety is a new benchmark for evaluating safety in multimodal-to-audio‑video generation. It covers all 11 non‑singleton conditioning configurations (text, image, audio, video) and contains 11,024 attack instances. The benchmark reveals that safety guards often fail when harmful semantics arise from combinations of benign inputs or when explicit harmful cues are masked by benign multimodal context, highlighting a gap in compositional risk perception.

By Kaichao Jiang, Changtao Miao, Baiqi Wu, Zhiyuan Lu, Kang Yang, Peiwei Zhao, Junchi Chen, Yunfeng Diao, He Liu, Qi Chu, Tao Gong, Nenghai Yu