Summary of Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout, by Anbin Qi et al.
Multimodal Emotion Recognition with Vision-language Prompting and Modality Dropout
by Anbin QI, Zhongliang Liu, Xinyong Zhou, Jinba Xiao, Fengrun Zhang, Qi Gan, Ming Tao, Gaozheng Zhang, Lu Zhang
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed solution for the Second Multimodal Emotion Recognition Challenge Track 1 (MER2024-SEMI) enhances the accuracy and generalization performance of emotion recognition by introducing several methods for multimodal emotion recognition. The model, EmoVCLIP, is fine-tuned based on CLIP using vision-language prompt learning, designed specifically for video-based emotion recognition tasks. This approach improves the performance of pre-trained CLIP on emotional videos. Additionally, modality dropout is employed to address modality dependence in multimodal fusion. Furthermore, GPT-4 is suggested as a prompt for Baichuan to aid in extracting emotional information. A self-training strategy is also utilized, leveraging unlabeled videos with high-confidence pseudo-labels generated by the model and incorporating them into the training set. The experimental results demonstrate that the proposed model ranks 1st in the MER2024-SEMI track, achieving an accuracy of 90.15% on the test set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new approach to recognizing emotions from videos. It uses a combination of computer vision and natural language processing techniques to improve the accuracy of emotion recognition. The model is tested on a dataset of emotional videos and performs well, achieving an accuracy of 90%. This could be useful in applications such as detecting people’s emotions in videos or improving customer service by recognizing when someone is upset. |
Keywords
» Artificial intelligence » Dropout » Generalization » Gpt » Natural language processing » Prompt » Self training
