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20232026
most citedMaintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning

1 citations · 2 across the 22 of their papers we have counts for

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cs.CV2026

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.…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2025

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…

cs.CV2024

Realistic Surgical Simulation from Monocular Videos

Kailing Wang, Chen Yang, Keyang Zhao +2

This paper tackles the challenge of automatically performing realistic surgical simulations from readily available surgical videos. Recent efforts have successfully integrated phys…

cs.CV2024

PosFormer: Recognizing Complex Handwritten Mathematical Expression with Position Forest Transformer

Tongkun Guan, Chengyu Lin, Wei Shen +1

Handwritten Mathematical Expression Recognition (HMER) has wide applications in human-machine interaction scenarios, such as digitized education and automated offices. Recently, se…

cs.CV2024★ 1 cited

Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning

Chongjie Si, Xuehui Wang, Xue Yang +6

Adapting pre-trained foundation models for various downstream tasks has been prevalent in artificial intelligence. Due to the vast number of tasks and high costs, adjusting all par…

cs.CV2024

Tendency-driven Mutual Exclusivity for Weakly Supervised Incremental Semantic Segmentation

Chongjie Si, Xuehui Wang, Xiaokang Yang +1

Weakly Incremental Learning for Semantic Segmentation (WILSS) leverages a pre-trained segmentation model to segment new classes using cost-effective and readily available image-lev…

cs.CV2024

EndoGSLAM: Real-Time Dense Reconstruction and Tracking in Endoscopic Surgeries using Gaussian Splatting

Kailing Wang, Chen Yang, Yuehao Wang +5

Precise camera tracking, high-fidelity 3D tissue reconstruction, and real-time online visualization are critical for intrabody medical imaging devices such as endoscopes and capsul…

cs.CV2023★ 1 cited

Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane

Chen Yang, Kailing Wang, Yuehao Wang +3

Intraoperative imaging techniques for reconstructing deformable tissues in vivo are pivotal for advanced surgical systems. Existing methods either compromise on rendering quality o…

cs.CV2023

Bridging Synthetic and Real Worlds for Pre-training Scene Text Detectors

Tongkun Guan, Wei Shen, Xue Yang +2

Existing scene text detection methods typically rely on extensive real data for training. Due to the lack of annotated real images, recent works have attempted to exploit large-sca…