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cs.CV2026
Latent Visual States for Efficient Multimodal Reasoning
Xiuwei Chen, Wentao Hu, Yongxin Wang +8
The integration of visual evidence has significantly enhanced the capabilities of large multimodal models. However, this integration predominantly relies on generating discrete out…
cs.CV2024
EACO: Enhancing Alignment in Multimodal LLMs via Critical Observation
Yongxin Wang, Meng Cao, Haokun Lin +5
Multimodal large language models (MLLMs) have achieved remarkable progress on various visual question answering and reasoning tasks leveraging instruction fine-tuning specific data…
cs.CV2024
Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs
Sukmin Yun, Haokun Lin, Rusiru Thushara +14
Multimodal large language models (MLLMs) have shown impressive success across modalities such as image, video, and audio in a variety of understanding and generation tasks. However…