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20242026
most citedNumerical Pruning for Efficient Autoregressive Models

1 citations · 2 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2026

MiLDEdit: Reasoning-Based Multi-Layer Design Document Editing

Zihao Lin, Wanrong Zhu, Jiuxiang Gu +8

Real-world design documents (e.g., posters) are inherently multi-layered, combining decoration, text, and images. Editing them from natural-language instructions requires fine-grai…

cs.AI2025

OIDA-QA: A Multimodal Benchmark for Analyzing the Opioid Industry Documents Archive

Xuan Shen, Brian Wingenroth, Zichao Wang +12

The opioid crisis represents a significant moment in public health that reveals systemic shortcomings across regulatory systems, healthcare practices, corporate governance, and pub…

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.LG2025

MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding

Siwei Han, Peng Xia, Ruiyi Zhang +4

Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Gener…

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…