Summary of Gcm-net: Graph-enhanced Cross-modal Infusion with a Metaheuristic-driven Network For Video Sentiment and Emotion Analysis, by Prasad Chaudhari et al.
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis
by Prasad Chaudhari, Aman Kumar, Chandravardhan Singh Raghaw, Mohammad Zia Ur Rehman, Nagendra Kumar
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 framework called GCM-Net that effectively addresses the challenges of sentiment analysis and emotion recognition in videos by leveraging multi-modal contextual information from utterances. The proposed approach integrates graph sampling and aggregation to recalibrate modality features for video-level sentiment and emotion prediction, while also employing a cross-modal attention module to determine intermodal interactions and utterance relevance. A harmonic optimization module using a metaheuristic algorithm combines attended features, allowing for handling both single and multi-utterance inputs. The framework is evaluated on three prominent benchmark datasets (CMU MOSI, CMU MOSEI, and IEMOCAP) achieving state-of-the-art results with accuracies of up to 91.56% for sentiment analysis and 85.66% for emotion recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand how people feel while watching videos by combining different types of information from the video, like what’s being said and shown. The method uses special techniques called graph sampling and aggregation to make sure all this information is used correctly. It also looks at which parts of the video are most important for understanding emotions and sentiments. This approach does really well on three big datasets, showing that it can understand videos better than other methods. |
Keywords
» Artificial intelligence » Attention » Multi modal » Optimization
