38 citations · 54 across the 27 of their papers we have counts for
22 papers · 1 filter
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…
MIV: Towards Efficient and Fine-grained Multimodal In-Context Learning via Representation Engineering
Yanshu Li, Yi Cao, Hongyang He +5
Multimodal in-context learning (ICL) equips Large Vision-language Models (LVLMs) with the ability to adapt to new tasks via multiple user-provided demonstrations, without requiring…
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization
Yingrui Ji, Xi Xiao, Gaofei Chen +5
Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success in cross-modal tasks such as zero-shot image classification and text-image retrieval by effectively al…
Visual Variational Autoencoder Prompt Tuning
Xi Xiao, Yunbei Zhang, Yanshuh Li +5
Parameter-efficient fine-tuning (PEFT) has emerged as a crucial approach for adapting large vision transformers to downstream tasks without the prohibitive computational costs of f…
MagicID: Hybrid Preference Optimization for ID-Consistent and Dynamic-Preserved Video Customization
Hengjia Li, Lifan Jiang, Xi Xiao +4
Video identity customization seeks to produce high-fidelity videos that maintain consistent identity and exhibit significant dynamics based on users' reference images. However, exi…
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
Xi Xiao, Zhengji Li, Wentao Wang +5
Object detection has witnessed remarkable advancements over the past decade, largely driven by breakthroughs in deep learning and the proliferation of large scale datasets. However…
Cycle-YOLO: A Efficient and Robust Framework for Pavement Damage Detection
Zhengji Li, Xi Xiao, Jiacheng Xie +5
With the development of modern society, traffic volume continues to increase in most countries worldwide, leading to an increase in the rate of pavement damage Therefore, the real-…
Multi-dimension Transformer with Attention-based Filtering for Medical Image Segmentation
Wentao Wang, Xi Xiao, Mingjie Liu +5
The accurate segmentation of medical images is crucial for diagnosing and treating diseases. Recent studies demonstrate that vision transformer-based methods have significantly imp…
Tailoring Adversarial Attacks on Deep Neural Networks for Targeted Class Manipulation Using DeepFool Algorithm
S. M. Fazle Rabby Labib, Joyanta Jyoti Mondal, Meem Arafat Manab +2
The susceptibility of deep neural networks (DNNs) to adversarial attacks undermines their reliability across numerous applications, underscoring the necessity for an in-depth explo…