Q-instruct: improving low-level visual abilities for multi-modality foundation models

Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model. While existing foundation models have shown exciting potentials on low-level...

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Bibliographic Details
Main Authors: Wu, Haoning, Zhang, Zicheng, Zhang, Erli, Chen, Chaofeng, Liao, Liang, Wang, Annan, Xu, Kaixin, Li, Chunyi, Hou, Jingwen, Zhai, Guangtao, Xue, Geng, Sun, Wenxiu, Yan, Qiong, Lin, Weisi
Other Authors: College of Computing and Data Science
Format: Conference or Workshop Item
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/178464
http://arxiv.org/abs/2311.06783v1
https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Q-Instruct_Improving_Low-level_Visual_Abilities_for_Multi-modality_Foundation_Models_CVPR_2024_paper.pdf
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Institution: Nanyang Technological University
Language: English