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cs.CV2025
START: Spatial and Textual Learning for Chart Understanding
Zhuoming Liu, Xiaofeng Gao, Feiyang Niu +3
Chart understanding is crucial for deploying multimodal large language models (MLLMs) in real-world scenarios such as analyzing scientific papers and technical reports. Unlike natu…
cs.CV2025
ProxT2I: Efficient Reward-Guided Text-to-Image Generation via Proximal Diffusion
Zhenghan Fang, Jian Zheng, Qiaozi Gao +2
Diffusion models have emerged as a dominant paradigm for generative modeling across a wide range of domains, including prompt-conditional generation. The vast majority of samplers,…
cs.CV2024
T2V-Turbo-v2: Enhancing Video Generation Model Post-Training through Data, Reward, and Conditional Guidance Design
Jiachen Li, Qian Long, Jian Zheng +4
In this paper, we focus on enhancing a diffusion-based text-to-video (T2V) model during the post-training phase by distilling a highly capable consistency model from a pretrained T…