1 citations · 1 across the 5 of their papers we have counts for
5 papers
LensVLM: Selective Context Expansion for Compressed Visual Representation of Text
Roy Xie, Dan Friedman, Donghan Yu +7
Vision Language Models (VLMs) offer the exciting possibility of processing text as rendered images, bypassing the need for tokenizing the text into long token sequences. Since VLM…
AstroMMBench: A Benchmark for Evaluating Multimodal Large Language Models Capabilities in Astronomy
Jinghang Shi, Xiaoyu Tang, Yang Huang +4
Astronomical image interpretation presents a significant challenge for applying multimodal large language models (MLLMs) to specialized scientific tasks. Existing benchmarks focus…
Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms
Zhangheng Li, Keen You, Haotian Zhang +7
Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limi…
InfoVisDial: An Informative Visual Dialogue Dataset by Bridging Large Multimodal and Language Models
Bingbing Wen, Zhengyuan Yang, Jianfeng Wang +3
In this paper, we build a visual dialogue dataset, named InfoVisDial, which provides rich informative answers in each round even with external knowledge related to the visual conte…
Text as Images: Can Multimodal Large Language Models Follow Printed Instructions in Pixels?
Xiujun Li, Yujie Lu, Zhe Gan +3
Recent multimodal large language models (MLLMs) have shown promising instruction following capabilities on vision-language tasks. In this work, we introduce VISUAL MODALITY INSTRUC…