[2603.13406] Nuanced Emotion Recognition Based on a Segment-based MLLM Framework Leveraging Qwen3-Omni for AH Detection

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.13406 (cs) [Submitted on 12 Mar 2026 (v1), last revised 23 Mar 2026 (this version, v2)] Title:Nuanced Emotion Recognition Based on a Segment-based MLLM Framework Leveraging Qwen3-Omni for AH Detection Authors:Liang Tang, Hongda Li, Jiayu Zhang, Long Chen, Shuxian Li, Siqi Pei, Tiaonan Duan, Yuhao Cheng View a PDF of the paper titled Nuanced Emotion Recognition Based on a Segment-based MLLM Framework Leveraging Qwen3-Omni for AH Detection, by Liang Tang and 7 other authors View PDF HTML (experimental) Abstract:Emotion recognition in videos is a pivotal task in affective computing, where identifying subtle psychological states such as Ambivalence and Hesitancy holds significant value for behavioral intervention and digital health. Ambivalence and Hesitancy states often manifest through cross-modal inconsistencies such as discrepancies between facial expressions, vocal tones, and textual semantics, posing a substantial challenge for automated recognition. This paper proposes a recognition framework that integrates temporal segment modeling with Multimodal Large Language Models. To address computational efficiency and token constraints in long video processing, we employ a segment-based strategy, partitioning videos into short clips with a maximum duration of 5 seconds. We leverage the Qwen3-Omni-30B-A3B model, fine-tuned on the BAH dataset using LoRA and full-parameter strategies via the MS-Swift framework,...