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X-VILA: Cross-Modality Alignment for Large Language Model

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arxiv 2405.19335 v1 pith:EEK5DJ5I submitted 2024-05-29 cs.CV cs.CLcs.LG

X-VILA: Cross-Modality Alignment for Large Language Model

Hanrong Ye , De-An Huang , Yao Lu , Zhiding Yu , Wei Ping , Andrew Tao , Jan Kautz , Song Han
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Dan Xu Pavlo Molchanov Hongxu Yin
This is my paper
classification cs.CV cs.CLcs.LG
keywords x-vilaalignmentcross-modalitylargevisualany-to-anyintroducelanguage
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We introduce X-VILA, an omni-modality model designed to extend the capabilities of large language models (LLMs) by incorporating image, video, and audio modalities. By aligning modality-specific encoders with LLM inputs and diffusion decoders with LLM outputs, X-VILA achieves cross-modality understanding, reasoning, and generation. To facilitate this cross-modality alignment, we curate an effective interleaved any-to-any modality instruction-following dataset. Furthermore, we identify a significant problem with the current cross-modality alignment method, which results in visual information loss. To address the issue, we propose a visual alignment mechanism with a visual embedding highway module. We then introduce a resource-efficient recipe for training X-VILA, that exhibits proficiency in any-to-any modality conversation, surpassing previous approaches by large margins. X-VILA also showcases emergent properties across modalities even in the absence of similar training data. The project will be made open-source.

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