X-VILA: Cross-Modality Alignment for Large Language Model
<|reference_start|>X-VILA: Cross-Modality Alignment for Large Language Model: 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.<|reference_end|>
@article{ye2024x-vila:,
    title={X-VILA: Cross-Modality Alignment for Large Language Model},
    author={Hanrong Ye, De-An Huang, Yao Lu, Zhiding Yu, Wei Ping, Andrew Tao, Jan
  Kautz, Song Han, Dan Xu, Pavlo Molchanov, Hongxu Yin},
    journal={arXiv preprint arXiv:2405.19335},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19335},
    primaryClass={cs.CV cs.CL cs.LG}
}