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We present the "Law of Vision Representation" in multimodal large language models (MLLMs).
Microsoft coco: Common objects in context
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Flamingo: a visual language model for few-shot learning
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Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation
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Learn to explain: Multimodal reasoning via thought chains for science question answering
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High-resolution image synthesis with latent diffusion models
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Qwen-vl: A frontier large vision-language model with versatile abilities
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Reproducible scaling laws for contrastive language-image learning
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Nvlm: Open frontier-class multimodal llms
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Convllava: Hierarchical backbones as visual encoder for large multimodal models
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Lightglue: Local feature matching at light speed
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Mmbench: Is your multi-modal model an all-around player?
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, et al · 2023
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Dinov2: Learning robust visual features without supervision
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Emu: Generative pretraining in multimodality
Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang · 2023
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Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, et al · 2024
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Qwen2.5 technical report, 2025
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Tulip: Towards unified language-image pretraining, 2025
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Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al · 2025
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