Feb. 9, 2024, 5:47 a.m. | Yunhao Gou Zhili Liu Kai Chen Lanqing Hong Hang Xu Aoxue Li Dit-Yan Yeung James T. Kwok

cs.CV updates on arXiv.org arxiv.org

Instruction tuning of the Large Vision-language Models (LVLMs) has revolutionized the development of versatile models with zero-shot generalization across a wide range of downstream vision-language tasks. However, diversity of training tasks of different sources and formats would lead to inevitable task conflicts, where different tasks conflicts for the same set of model parameters, resulting in sub-optimal instruction-following abilities. To address that, we propose the Mixture of Cluster-conditional LoRA Experts (MoCLE), a novel Mixture of Experts (MoE) architecture designed to activate …

cluster cs.cv development diversity experts language language models lora set tasks training vision vision-language models zero-shot

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