Summary of Contrastive Learning-based Multi Modal Architecture For Emoticon Prediction by Employing Image-text Pairs, By Ananya Pandey et al.
Contrastive Learning-based Multi Modal Architecture for Emoticon Prediction by Employing Image-Text Pairs
by Ananya Pandey, Dinesh Kumar Vishwakarma
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 research explores the relationship between sentences, visuals, and emoticons in multimodal messages, aiming to analyze how these elements interact. The study proposes a novel contrastive learning-based architecture that combines textual and visual information to convey meaning. By joint training of dual-branch encoders and contrastive learning, the model maps text and images into a common latent space, achieving superior results compared to existing approaches. The proposed methodology attains an accuracy of 91% and MCC-score of 90% when assessing emoticons using the Multimodal-Twitter Emoticon dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how we use emojis in messages to convey meaning. It’s like trying to understand a message with multiple parts – words, pictures, and symbols. The researchers developed a new way to combine these elements, called contrastive learning, which helps machines better understand what we mean when we use emojis. They tested this method on a big dataset of Twitter messages and found it worked really well, achieving an accuracy rate of 91%. This is important because it can help computers better understand human communication. |
Keywords
» Artificial intelligence » Latent space
