Summary of Emotion-llama: Multimodal Emotion Recognition and Reasoning with Instruction Tuning, by Zebang Cheng et al.
Emotion-LLaMA: Multimodal Emotion Recognition and Reasoning with Instruction Tuning
by Zebang Cheng, Zhi-Qi Cheng, Jun-Yan He, Jingdong Sun, Kai Wang, Yuxiang Lin, Zheng Lian, Xiaojiang Peng, Alexander Hauptmann
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to accurate emotion perception by introducing a multimodal dataset and a model that seamlessly integrates audio, visual, and textual inputs. The MERR dataset contains 28,618 coarse-grained and 4,487 fine-grained annotated samples across diverse emotional categories, enabling models to learn from varied scenarios and generalize to real-world applications. The proposed Emotion-LLaMA model uses emotion-specific encoders to align features into a shared space, improving both emotional recognition and reasoning capabilities. Evaluations show that Emotion-LLaMA outperforms other Multimodal Large Language Models (MLLMs) on various benchmarks, including Clue Overlap, Label Overlap, EMER, MER2023-SEMI challenge, and DFEW dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Emotion perception is important for many applications. Currently, most methods only use one way of communicating emotions, like facial expressions or voice tone. But in real life, people express emotions in multiple ways. To solve this problem, the authors created a big dataset called MERR that has lots of examples of different emotional categories. They also developed a new model called Emotion-LLaMA that combines audio, visual, and textual information to better recognize emotions. This model performed well on many tests, showing its ability to generalize to real-world situations. |
Keywords
» Artificial intelligence » Llama
