activity
20232026
most citedInformation-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Control

38 citations · 54 across the 27 of their papers we have counts for

collaborators

36 papers

cs.CV2026

MedSTP: Preserving Subspace and Transition Evidence for Medical VQA Token Pruning

Mengchen Fan, Qizhen Lan, Xi Xiao +6

Medical vision-language models (VLMs) for visual question answering (VQA) often pass long visual-token sequences to the language model, making visual-token pruning attractive but r…

cs.LG2026

Posterior Flow Matching

Chenrui Ma, Xi Xiao, Lin Zhao +4

Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an intermediate state. This paper in…

cs.CV2026

Dynamic Hub-and-Spoke Memory for Streaming Video Understanding

Xinru Jiang, Lin Zhao, Xi Xiao +7

Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to compactly remember long-range h…

cs.CV2026

Rethinking Layer-Wise Information Allocation for Vision Foundation Model Adaptation

Yuqi Li, Xi Xiao, Yunbei Zhang +6

Vision foundation models are increasingly reused as frozen backbones for downstream visual recognition, making parameter-efficient adaptation a central problem. Prompt-based adapta…

cs.LG2026

Less Tokens, Better Forecasts: Sparse Residual Routing for Efficient Weather Prediction

Janet Wang, Yunbei Zhang, Lin Zhao +3

Existing ViT-based weather forecasting models apply uniform computation across all spatial tokens, even though nearby atmospheric grid points often contain similar values and large…

cs.CV2026

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

Xi Xiao, Chenrui Ma, Yunbei Zhang +7

Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: semantic drift, by trea…

cs.CV2025

Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection

Xi Xiao, Zhuxuanzi Wang, Mingqiao Mo +6

The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require cost…

cs.LG2025

CTR-LoRA: Curvature-Aware and Trust-Region Guided Low-Rank Adaptation for Large Language Models

Zhuxuanzi Wang, Mingqiao Mo, Xi Xiao +6

Parameter-efficient fine-tuning (PEFT) has become the standard approach for adapting large language models under limited compute and memory budgets. Although previous methods impro…

cs.CV2025

Towards Foundation Models for Cryo-ET Subtomogram Analysis

Runmin Jiang, Wanyue Feng, Yuntian Yang +11

Cryo-electron tomography (cryo-ET) enables in situ visualization of macromolecular structures, where subtomogram analysis tasks such as classification, alignment, and averaging are…

cs.CV2025★ 1 cited

Prompt-based Adaptation in Large-scale Vision Models: A Survey

Xi Xiao, Yunbei Zhang, Lin Zhao +12

In computer vision, Visual Prompting (VP) and Visual Prompt Tuning (VPT) have recently emerged as lightweight and effective alternatives to full fine-tuning for adapting large-scal…

cs.LG2025

HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance

Hao Zhang, Zhenjia Li, Runfeng Bao +8

Parameter-Efficient Fine-Tuning (PEFT), especially Low-Rank Adaptation (LoRA), has emerged as a promising approach to fine-tuning large language models(LLMs) while reducing computa…

cs.CV2025

Stochastic Interpolants via Conditional Dependent Coupling

Chenrui Ma, Xi Xiao, Tianyang Wang +2

Existing image generation models face critical challenges regarding the trade-off between computation and fidelity. Specifically, models relying on a pretrained Variational Autoenc…

cs.LG2025

Sensitivity-LoRA: Low-Load Sensitivity-Based Fine-Tuning for Large Language Models

Hao Zhang, Bo Huang, Zhenjia Li +6

Large Language Models (LLMs) have transformed both everyday life and scientific research. However, adapting LLMs from general-purpose models to specialized tasks remains challengin…

cs.RO2025★ 38 cited

Information-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Control

Shunlei Li, Longsen Gao, Jin Wang +5

Teaching robots dexterous skills from human videos remains challenging due to the reliance on low-level trajectory imitation, which fails to generalize across object types, spatial…

cs.CV2025

Visual Instance-aware Prompt Tuning

Xi Xiao, Yunbei Zhang, Xingjian Li +5

Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that rema…

cs.CE2025

RoadBench: A Vision-Language Foundation Model and Benchmark for Road Damage Understanding

Xi Xiao, Yunbei Zhang, Janet Wang +9

Accurate road damage detection is crucial for timely infrastructure maintenance and public safety, but existing vision-only datasets and models lack the rich contextual understandi…

cs.CV2025

FOCUS: Fused Observation of Channels for Unveiling Spectra

Xi Xiao, Aristeidis Tsaris, Anika Tabassum +4

Hyperspectral imaging (HSI) captures hundreds of narrow, contiguous wavelength bands, making it a powerful tool in biology, agriculture, and environmental monitoring. However, inte…

cs.CL2025

ETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding

Lijing Zhu, Qizhen Lan, Qing Tian +8

Continual Knowledge Graph Embedding (CKGE) seeks to integrate new knowledge while preserving past information. However, existing methods struggle with efficiency and scalability du…

cs.CV2025

Beyond Editing Pairs: Fine-Grained Instructional Image Editing via Multi-Scale Learnable Regions

Chenrui Ma, Xi Xiao, Tianyang Wang +1

Current text-driven image editing methods typically follow one of two directions: relying on large-scale, high-quality editing pair datasets to improve editing precision and divers…

cs.CV2025★ 2 cited

Describe Anything in Medical Images

Xi Xiao, Yunbei Zhang, Thanh-Huy Nguyen +10

Localized image captioning has made significant progress with models like the Describe Anything Model (DAM), which can generate detailed region-specific descriptions without explic…

cs.CV2025

TWIG: Two-Step Image Generation using Segmentation Masks in Diffusion Models

Mazharul Islam Rakib, Showrin Rahman, Joyanta Jyoti Mondal +4

In today's age of social media and marketing, copyright issues can be a major roadblock to the free sharing of images. Generative AI models have made it possible to create high-qua…

cs.CV2025

CryoCCD: Conditional Cycle-consistent Diffusion with Biophysical Modeling for Cryo-EM Synthesis

Runmin Jiang, Genpei Zhang, Yuntian Yang +10

Single-particle cryo-electron microscopy (cryo-EM) has become a cornerstone of structural biology, enabling near-atomic resolution analysis of macromolecules through advanced compu…

cs.LG2025

MoRE-Brain: Routed Mixture of Experts for Interpretable and Generalizable Cross-Subject fMRI Visual Decoding

Yuxiang Wei, Yanteng Zhang, Xi Xiao +3

Decoding visual experiences from fMRI offers a powerful avenue to understand human perception and develop advanced brain-computer interfaces. However, current progress often priori…

cs.LG2025★ 3 cited

ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling

Xiao Wang, Jong-Youl Choi, Takuya Kurihaya +15

Sparse observations and coarse-resolution climate models limit effective regional decision-making, underscoring the need for robust downscaling. However, existing AI methods strugg…