4 citations · 4 across the 12 of their papers we have counts for
7 papers · 1 filter
OmniMapBench: Benchmarking Visual-Centric Reasoning on Diverse Map Documents
Yang Chen, Yunwen Li, Yufan Shen +6
Recent advancements in LVLMs necessitate robust benchmarks for complex, visually grounded reasoning. A critical limitation is identified in many document understanding benchmarks:…
HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding
Wenhui Liao, Hongliang Li, Pengyu Xie +15
Document parsing is a fundamental task in multimodal understanding, supporting a wide range of downstream applications such as information extraction and intelligent document analy…
UR-Bench: A Benchmark for Multi-Hop Reasoning over Ultra-High-Resolution Images
Siqi Li, Xinyu Cai, Jianbiao Mei +7
Recent multimodal large language models (MLLMs) show strong capabilities in visual-language reasoning, yet their performance on ultra-high-resolution imagery remains largely unexpl…
IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video?
Yang Chen, Minghao Liu, Yufan Shen +18
The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code. However, existing benchmarks primarily focus on static sc…
Learning Only with Images: Visual Reinforcement Learning with Reasoning, Rendering, and Visual Feedback
Yang Chen, Yufan Shen, Wenxuan Huang +7
Multimodal Large Language Models (MLLMs) exhibit impressive performance across various visual tasks. Subsequent investigations into enhancing their visual reasoning abilities have…
ProcTag: Process Tagging for Assessing the Efficacy of Document Instruction Data
Yufan Shen, Chuwei Luo, Zhaoqing Zhu +5
Recently, large language models (LLMs) and multimodal large language models (MLLMs) have demonstrated promising results on document visual question answering (VQA) task, particular…
LayoutLLM: Layout Instruction Tuning with Large Language Models for Document Understanding
Chuwei Luo, Yufan Shen, Zhaoqing Zhu +3
Recently, leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However, previous works tha…