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

• URL: http://arxiv.org/abs/2405.19335v1
• Date: Wed, 29 May 2024 17:59:58 GMT
• Title: X-VILA: Cross-Modality Alignment for Large Language Model
• Authors: Hanrong Ye, De-An Huang, Yao Lu, Zhiding Yu, Wei Ping, Andrew Tao, Jan Kautz, Song Han, Dan Xu, Pavlo Molchanov, Hongxu Yin,
• Abstract summary: X-VILA is an omni-modality model designed to extend the capabilities of large language models (LLMs) by incorporating image, video, and audio modalities. We propose a visual alignment mechanism with a visual embedding highway module to address the problem of visual information loss. X-VILA exhibits proficiency in any-to-any modality conversation, surpassing previous approaches by large margins.
• Score: 91.96081978952283
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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