27 citations · 40 across the 15 of their papers we have counts for
8 papers · 1 filter
Numerical Pruning for Efficient Autoregressive Models
Xuan Shen, Zhao Song, Yufa Zhou +12
Transformers have emerged as the leading architecture in deep learning, proving to be versatile and highly effective across diverse domains beyond language and image processing. Ho…
SUGAR: Subject-Driven Video Customization in a Zero-Shot Manner
Yufan Zhou, Ruiyi Zhang, Jiuxiang Gu +3
We present SUGAR, a zero-shot method for subject-driven video customization. Given an input image, SUGAR is capable of generating videos for the subject contained in the image and…
SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding
Jian Chen, Ruiyi Zhang, Yufan Zhou +6
Multimodal large language models (MLLMs) have recently shown great progress in text-rich image understanding, yet they still struggle with complex, multi-page visually-rich documen…
LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models
Ruiyi Zhang, Yufan Zhou, Jian Chen +3
Large multimodal language models have demonstrated impressive capabilities in understanding and manipulating images. However, many of these models struggle with comprehending inten…
DocSynthv2: A Practical Autoregressive Modeling for Document Generation
Sanket Biswas, Rajiv Jain, Vlad I. Morariu +5
While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge. Th…
TRINS: Towards Multimodal Language Models that Can Read
Ruiyi Zhang, Yanzhe Zhang, Jian Chen +4
Large multimodal language models have shown remarkable proficiency in understanding and editing images. However, a majority of these visually-tuned models struggle to comprehend th…
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models
Jianyi Zhang, Yufan Zhou, Jiuxiang Gu +5
Diffusion models have demonstrated exceptional capabilities in generating a broad spectrum of visual content, yet their proficiency in rendering text is still limited: they often g…
Toffee: Efficient Million-Scale Dataset Construction for Subject-Driven Text-to-Image Generation
Yufan Zhou, Ruiyi Zhang, Kaizhi Zheng +5
In subject-driven text-to-image generation, recent works have achieved superior performance by training the model on synthetic datasets containing numerous image pairs. Trained on…