Summary of Turbo Your Multi-modal Classification with Contrastive Learning, by Zhiyu Zhang and Da Liu and Shengqiang Liu and Anna Wang and Jie Gao and Yali Li
Turbo your multi-modal classification with contrastive learning
by Zhiyu Zhang, Da Liu, Shengqiang Liu, Anna Wang, Jie Gao, Yali Li
First submitted to arxiv on: 14 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposes a novel approach to multi-modal representation learning called Turbo, which combines in-modal and cross-modal contrastive learning. The authors aim to improve the understanding of each modality by introducing a new strategy that uses two different representations for each modality, obtained through hidden dropout masks. This is achieved by sending multi-modal data pairs through the forward pass twice, resulting in multiple contrastive objectives for training. The proposed method, Turbo, is evaluated on two audio-text classification tasks and achieves state-of-the-art performance on a speech emotion recognition benchmark dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big discovery about how computers can understand different types of data, like text and sound. Right now, most computers are good at understanding one type of data but not the other. The researchers found a new way to teach computers to understand both types of data by comparing them to each other. They call this method “Turbo” and tested it on two big datasets. It worked really well and beat all the other methods in recognizing emotions from speech. |
Keywords
* Artificial intelligence * Dropout * Multi modal * Representation learning * Text classification
