Summary of Oneencoder: a Lightweight Framework For Progressive Alignment Of Modalities, by Bilal Faye et al.
OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities
by Bilal Faye, Hanane Azzag, Mustapha Lebbah
First submitted to arxiv on: 17 Sep 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 The proposed OneEncoder framework addresses the limitations of current cross-modal alignment learning techniques by introducing a lightweight approach that progressively represents and aligns four modalities: image, text, audio, and video. The framework initially trains a Universal Projection module to align image and text modalities, then freezes this pre-trained module and aligns future modalities to those already aligned. This design enables efficient and cost-effective operation, even in scenarios where vast aligned datasets are unavailable. OneEncoder demonstrates strong performance in tasks like classification, querying, and visual question answering, surpassing methods that rely on large datasets and specialized encoders. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OneEncoder is a new way to combine different types of information, like pictures, words, sounds, and videos. This helps machines understand and work with different kinds of data better. Right now, making these combinations requires big amounts of special training data and powerful computers. But OneEncoder makes it possible to do this even with limited data and resources. It’s a simple and efficient way to combine information from different sources, which can be very useful in tasks like recognizing objects, answering questions, or analyzing audiovisual content. |
Keywords
» Artificial intelligence » Alignment » Classification » Question answering
