2 citations · 4 across the 22 of their papers we have counts for
32 papers
MCD: Causal Distillation of Multimodal In-Context Learning in Large Vision-Language Models
Yanshu Li, Jiaqian Li, Canran Xiao +3
Large vision-language models (LVLMs) exhibit strong multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as model size decreases. Knowledge di…
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…
Adapting Vision Foundation Models with Cascaded Semantics
Xi Xiao, Xingjian Li, Cheng Han +8
Prompt tuning, a leading parameter-efficient adaptation paradigm in NLP, has recently been extended to computer vision. Visual prompt tuning (VPT) adapts pre-trained vision transfo…
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…
Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs
Xi Xiao, Chen Liu, Chih-Ting Liao +9
Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text. Despite inheriting strong reason…
Layer-Specific Prompt Fusion Discovery via Differentiable Search in Vision Foundation Models
Xi Xiao, Xingjian Li, Yunbei Zhang +7
Visual prompt tuning has emerged as a parameter-efficient fine-tuning approach for adapting large-scale Vision Transformers (ViTs) to downstream tasks. As its learnable prompts are…
SpaMEM: Benchmarking Dynamic Spatial Reasoning via Perception-Memory Integration in Embodied Environments
Chih-Ting Liao, Xi Xiao, Chunlei Meng +6
Multimodal large language models (MLLMs) have advanced static visual--spatial reasoning, yet they often fail to preserve long-horizon spatial coherence in embodied settings where b…
Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation
Mengchen Fan, Baocheng Geng, Xi Xiao +5
Deploying high-performing 3D medical image segmenters (e.g., nnU-Net) is often limited by memory footprint and inference latency. Compression is therefore necessary, but compact 3D…
Drift Flow Matching
Chenrui Ma, Xi Xiao, Lin Zhao +3
Iterative generative models such as Flow Matching and Diffusion models have demonstrated strong test-time scaling behavior, where additional inference computation can improve gener…
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…
Unified Multi-Foundation-Model Slide Representation for Pan-Cancer Recognition and Text-Guided Tumor Localization
Tianyang Wang, Ziyu Su, Abdul Rehman Akbar +6
The expanding ecosystem of pathology foundation models has produced powerful but fragmented tile-level representations, limiting their use in clinical tasks that require unified sl…
Dispersion Loss Counteracts Embedding Condensation and Improves Generalization in Small Language Models
Chen Liu, Xingzhi Sun, Xi Xiao +8
Large language models (LLMs) achieve remarkable performance through ever-increasing parameter counts, but scaling incurs steep computational costs. To better understand LLM scaling…
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…
Learning Straight Flows: Variational Flow Matching for Efficient Generation
Chenrui Ma, Xi Xiao, Tianyang Wang +2
Flow Matching has limited ability in achieving one-step generation due to its reliance on learned curved trajectories. Previous studies have attempted to address this limitation by…
fMRI-LM: Towards a Universal Foundation Model for Language-Aligned fMRI Understanding
Yuxiang Wei, Yanteng Zhang, Xi Xiao +3
Recent advances in multimodal large language models (LLMs) have enabled unified reasoning across images, audio, and video, but extending such capability to brain imaging remains la…
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…
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…
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…
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…
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…
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…
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…
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…
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…