1 citations · 2 across the 22 of their papers we have counts for
25 papers
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.…
AdaThinking-E: One-Token Entropy Regulation for Adaptive Thinking
Zining Wang, Tongkun Guan, Boming Chen +8
Multimodal large language models have demonstrated strong document reasoning capabilities by incorporating explicit thinking processes. While this capability significantly improves…
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…
AdaMuon: Adaptive Muon Optimizer
Chongjie Si, Debing Zhang, Wei Shen
We propose AdaMuon, a novel optimizer that combines element-wise adaptivity with orthogonal updates for large-scale neural network training. AdaMuon incorporates two tightly couple…
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…
Weight Spectra Induced Efficient Model Adaptation
Chongjie Si, Xuankun Yang, Muqing Liu +5
Large-scale foundation models have demonstrated remarkable versatility across a wide range of downstream tasks. However, fully fine-tuning these models incurs prohibitive computati…
MAP: Revisiting Weight Decomposition for Low-Rank Adaptation
Chongjie Si, Zhiyi Shi, Yadao Wang +3
The rapid development of large language models has revolutionized natural language processing, but their fine-tuning remains computationally expensive, hindering broad deployment.…
Revisiting Sparsity Constraint Under High-Rank Property in Partial Multi-Label Learning
Chongjie Si, Yidan Cui, Fuchao Yang +2
Partial Multi-Label Learning (PML) extends the multi-label learning paradigm to scenarios where each sample is associated with a candidate label set containing both ground-truth la…
Why Can Accurate Models Be Learned from Inaccurate Annotations?
Chongjie Si, Yidan Cui, Fuchao Yang +2
Learning from inaccurate annotations has gained significant attention due to the high cost of precise labeling. However, despite the presence of erroneous labels, models trained on…
NAN: A Training-Free Solution to Coefficient Estimation in Model Merging
Chongjie Si, Kangtao Lv, Jingjing Jiang +6
Model merging offers a training-free alternative to multi-task learning by combining independently fine-tuned models into a unified one without access to raw data. However, existin…
Generalized Tensor-based Parameter-Efficient Fine-Tuning via Lie Group Transformations
Chongjie Si, Zhiyi Shi, Xuehui Wang +3
Adapting pre-trained foundation models for diverse downstream tasks is a core practice in artificial intelligence. However, the wide range of tasks and high computational costs mak…
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…
Unveiling the Mystery of Weight in Large Foundation Models: Gaussian Distribution Never Fades
Chongjie Si, Jingjing Jiang, Wei Shen
This paper presents a pioneering exploration of the mechanisms underlying large foundation models' (LFMs) weights, aiming to simplify AI research. Through extensive observation and…
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…
Task-Specific Directions: Definition, Exploration, and Utilization in Parameter Efficient Fine-Tuning
Chongjie Si, Zhiyi Shi, Shifan Zhang +3
Large language models demonstrate impressive performance on downstream tasks, yet they require extensive resource consumption when fully fine-tuning all parameters. To mitigate thi…
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…
See Further for Parameter Efficient Fine-tuning by Standing on the Shoulders of Decomposition
Chongjie Si, Xiaokang Yang, Wei Shen
The rapid expansion of large foundation models within the pre-training and fine-tuning framework has underscored that larger models often yield better results. However, the scaling…
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…
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…
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…
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…
Appeal: Allow Mislabeled Samples the Chance to be Rectified in Partial Label Learning
Chongjie Si, Xuehui Wang, Yan Wang +2
In partial label learning (PLL), each instance is associated with a set of candidate labels among which only one is ground-truth. The majority of the existing works focuses on cons…