most citedDescribe Anything in Medical Images

2 citations · 3 across the 9 of their papers we have counts for

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cs.CV2026

BluePixel: Toward Native 4K Agentic Image Editing

Shanyan Guan, Shiyu Li, Xin Li +17

Instruction-driven image editing has reached native 4K, yet how such editors are trained remains undisclosed, and pretraining one from scratch is beyond most teams. The task itself…

cs.CV2026

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…

cs.CV2026

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…

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.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.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.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.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

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.CV2025

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…