Summary of X-vila: Cross-modality Alignment For Large Language Model, by Hanrong Ye et al.
X-VILA: Cross-Modality Alignment for Large Language Model
by Hanrong Ye, De-An Huang, Yao Lu, Zhiding Yu, Wei Ping, Andrew Tao, Jan Kautz, Song Han, Dan Xu, Pavlo Molchanov, Hongxu Yin
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 X-VILA model is an omni-modality extension to large language models (LLMs) that incorporates image, video, and audio inputs. By aligning modality-specific encoders with LLM inputs and diffusion decoders with LLM outputs, X-VILA achieves cross-modality understanding, reasoning, and generation. The model relies on a curated dataset for instruction-following and addresses visual information loss through a proposed visual alignment mechanism. Experimental results show that X-VILA surpasses previous approaches in any-to-any modality conversation while exhibiting emergent properties across modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The X-VILA model is an innovative way to make language models work with images, videos, and audio files. It helps computers understand and generate text related to different types of media. The researchers created a special dataset for this task and found a solution to a problem that makes visual information disappear when combining languages and images. This new approach performs much better than previous ones and can even create new connections between different types of data without being trained on them. |
Keywords
» Artificial intelligence » Alignment » Diffusion
