10 citations · 20 across the 18 of their papers we have counts for
7 papers · 1 filter
HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence
Fei Ma, Zebang Cheng, Minghui Li +11
Visual intelligence seeks to perceive, interpret, and synthesize the visual world and is central to modern computer vision. Human-centered visual intelligence is especially demandi…
MOPDA: Mixed-Trajectory On-Policy Distillation for Language-Guided Industrial Anomaly Detection
Shuimu Chen, Jing Jin, Nan Su +5
Large vision-language models (LVLMs) have shown strong potential for industrial anomaly detection (IAD) by providing image-level anomaly judgments and interpretable reasoning. Howe…
OmniOPSD: Rationale-Privileged On-Policy Self-Distillation for Affective Computing
Zebang Cheng, Shuimu Chen, Boxue Yang +7
Reinforcement learning for multimodal large language models (MLLMs) is often hindered by severe reward sparsity in complex reasoning tasks. This challenge is particularly pronounce…
Reflect-R1: Evidence-Driven Reflection for Self-Correction in Long Video Understanding
Shuimu Chen, Yuteng Chen, Yuanshen Guan +7
Current multimodal reflection mechanisms for long video understanding predominantly rely on closed-loop self-reflection within internal parameters. Lacking objective external evide…
EmoTrans: A Benchmark for Understanding, Reasoning, and Predicting Emotion Transitions in Multimodal LLMs
He Hu, Tengjin Weng, Zebang Cheng +5
Recent multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and generation, and are increasingly used in applications such as social ro…
MER 2026: From Discriminative Emotion Recognition to Generative Emotion Understanding
Zheng Lian, Xiaojiang Peng, Kele Xu +16
MER2026 marks the fourth edition of the MER series of challenges. The MER series provides valuable data resources to the research community and offers tasks centered on recent rese…
Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding
Xiaojiang Peng, Jingyi Chen, Zebang Cheng +11
Understanding human emotions from multimodal signals poses a significant challenge in affective computing and human-robot interaction. While multimodal large language models (MLLMs…