Papers by Yunzhe Tao
InfiMM: Advancing Multimodal Understanding with an Open-Sourced Visual Language Model (2024.findings-acl)
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Haogeng Liu, Quanzeng You, Yiqi Wang, Xiaotian Han, Bohan Zhai, Yongfei Liu, Wentao Chen, Yiren Jian, Yunzhe Tao, Jianbo Yuan, Ran He, Hongxia Yang
| Challenge: | InfiMM is a multimodal large language model that adapts to complex vision-language tasks. |
| Approach: | They present a Multimodal Large Language Model that adapts to intricate vision-language tasks using large-scale training data and comprehensive training strategies. |
| Outcome: | Empirical evaluations across a variety of benchmarks underscore InfiMM’s remarkable capability in multimodal understanding. |
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction (2024.acl-long)
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| Challenge: | EVLGen is a framework for visual-language pre-training with high computational demands. |
| Approach: | They propose a streamlined framework for the pre-training of visually conditioned language generation models with high computational demands. |
| Outcome: | The proposed framework accelerates training of vision-language models by a factor of 5 without compromising performance. |