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arXiv AI By Yibo Wang, Rui Yang, Jisheng Dang, Bimei Wang, Yitao Wu, Pengfei Cao, Wencan Zhang, Hong Peng, Bin Hu, Tat-Seng Chua

AffectOmni: RL-Verifiable People-Centric Grounded Affective Reasoning for Social and Art-Related Scenes

Read the original on arXiv AI →

AffectOmni is a reinforcement‑learning‑trained framework that enhances multimodal large language models for affective reasoning in social and art‑related scenes. It introduces People Focus and Temporal Order rewards to prioritize people‑centric cues and structured reasoning, and uses within‑group comparative scoring for more discriminative rewards. A Thinking Summarizer converts rationales into executable evidence instructions, which are grounded into pixel‑level regions via SAM3, enabling external auditability.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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