11 papers · 1 filter
VaRS-Doc: Interpretation-Aware Variant Representations via Latent Self-Probing for Visual Document Retrieval
Haocheng Wang, Tongkun Guan, Wei Shen +1
Visual document retrieval has recently become increasingly important in applications such as enterprise search, scientific literature discovery, and retrieval-augmented generation.…
Combating Textual Noise and Redundancy: Entropy-Aware Dense Visual Token Pruning
Xuehui Wang, Xuankun Yang, Wei Shen
Visual token pruning is a crucial strategy for accelerating VLMs by compressing redundant image patches, yet existing methods often fail to preserve critical cues under dense instr…
LightSTAR: Efficient Visual Document Retrieval via Lightweight Selection with Vision-Adaptive Refinement
Tongkun Guan, Haocheng Wang, Wei Shen +1
Visual document retrieval requires rapidly locating relevant pages from large multi-modal corpora in response to user queries. While recent methods powered by Multi-modal Large Lan…
MedSeg-R: Reasoning Segmentation in Medical Images with Multimodal Large Language Models
Yu Huang, Zelin Peng, Yichen Zhao +3
Medical image segmentation is crucial for clinical diagnosis, yet existing models are limited by their reliance on explicit human instructions and lack the active reasoning capabil…
Marten: Visual Question Answering with Mask Generation for Multi-modal Document Understanding
Zining Wang, Tongkun Guan, Pei Fu +7
Multi-modal Large Language Models (MLLMs) have introduced a novel dimension to document understanding, i.e., they endow large language models with visual comprehension capabilities…
A Token-level Text Image Foundation Model for Document Understanding
Tongkun Guan, Zining Wang, Pei Fu +9
In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). H…