most citedNumerical Pruning for Efficient Autoregressive Models

1 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Scaling Up Audio-Synchronized Visual Animation: An Efficient Training Paradigm

Lin Zhang, Zefan Cai, Yufan Zhou +10

Recent advances in audio-synchronized visual animation enable control of video content using audios from specific classes. However, existing methods rely heavily on expensive manua…

cs.CV2025

Towards Visual Text Grounding of Multimodal Large Language Model

Ming Li, Ruiyi Zhang, Jian Chen +7

Despite the existing evolution of Multimodal Large Language Models (MLLMs), a non-neglectable limitation remains in their struggle with visual text grounding, especially in text-ri…

cs.LG20241 cited

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…

cs.CV20241 cited

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…

cs.CV2024

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…