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[2601.16449] Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding
Summary
The paper introduces Emotion-LLaMAv2 and MMEVerse, a new framework and benchmark aimed at enhancing multimodal emotion understanding through advanced machine learning techniques.
Why It Matters
This research addresses significant gaps in the field of affective computing, particularly the need for high-quality datasets and standardized benchmarks. By improving emotional reasoning capabilities in multimodal large language models, it has implications for applications in human-robot interaction and emotional AI.
Key Takeaways
- Emotion-LLaMAv2 enhances emotional reasoning in multimodal models.
- MMEVerse aggregates multiple datasets for unified emotion recognition.
- The framework eliminates reliance on external face detection for better accuracy.
- Introduces a novel curriculum instruction tuning scheme for emotion reasoning.
- Aims to standardize evaluation methods in the field of multimodal emotion understanding.
Computer Science > Computer Vision and Pattern Recognition arXiv:2601.16449 (cs) [Submitted on 23 Jan 2026 (v1), last revised 23 Feb 2026 (this version, v2)] Title:Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding Authors:Xiaojiang Peng, Jingyi Chen, Zebang Cheng, Bao Peng, Fengyi Wu, Yifei Dong, Shuyuan Tu, Qiyu Hu, Huiting Huang, Yuxiang Lin, Jun-Yan He, Kai Wang, Zheng Lian, Zhi-Qi Cheng View a PDF of the paper titled Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding, by Xiaojiang Peng and 13 other authors View PDF HTML (experimental) Abstract:Understanding human emotions from multimodal signals poses a significant challenge in affective computing and human-robot interaction. While multimodal large language models (MLLMs) have excelled in general vision-language tasks, their capabilities in emotional reasoning remain limited. The field currently suffers from a scarcity of large-scale datasets with high-quality, descriptive emotion annotations and lacks standardized benchmarks for evaluation. Our preliminary framework, Emotion-LLaMA, pioneered instruction-tuned multimodal learning for emotion reasoning but was restricted by explicit face detectors, implicit fusion strategies, and low-quality training data with limited scale. To address these limitations, we present Emotion-LLaMAv2 and the MMEVerse benchmark, establishing an end-to-end pipeline together with a standardized evalua...
