Vision Foundation Model Adaptation

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Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation
Jun 20, 2026Qing Xu, Xiangjian He, Wenting Duan +2Vision Foundation Model Adaptation Medical Image Segmentation
School of Computer Science, University of Nottingham, UK · School of Computer Science, University of Nottingham Ningbo China, China · School of Engineering and Physical Science, University of Lincoln, UK +2
$μ$ Match: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM
Jun 19, 2026Marei Freitag, Olesia Korchevaia, Luca Freckmann +2Unsupervised Domain Adaptation Vision Foundation Model Adaptation
Life and Medical Sciences Institute (LIMES), University of Bonn, Germany · Institute of Computer Science, Georg-August-University Göttingen, Germany
Language-Instructed Vision Embeddings for Controllable and Generalizable Perception
Jun 17, 2026Chengzhi Mao, Xudong Lin, Wen-Sheng ChuVisual Representation Learning Vision Foundation Model Adaptation
Google
DINO-Med3D: Bridging Dimension and Domain Gaps in Volumetric Segmentation via Progressive Adaptation
Jun 17, 2026Haoyu Hu, Xiyao Ma, Shiqi Liu +43D ViTs Domain Adaptation
University of Chinese Academy of Sciences, Beijing, China · Institute of Automation, Chinese Academy of Sciences, Beijing, China
Geometry-Consistent Endoscopic Representations for Image-Guided Navigation via Structured Foundation Model Adaptation
Jun 15, 2026Hongchao Shu, Roger D. Soberanis-Mukul, Hao Ding +5Endoscopy Monocular Depth Estimation
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21211 USA · Semaphor Surgical · Johnson & Johnson MedTech, Santa Clara, CA 95054 USA
Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts
Jun 14, 2026Aniq Ahmad, Heather Bedle, Ahmad MustafaPromptable Image Segmentation Vision Foundation Model Adaptation
School of Geosciences, University of Oklahoma, Norman, Oklahoma, USA · King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Task-Instructed Causal Routing of Vision Foundation Models for Multi-Task Learning
Jun 14, 2026Donghyun Han, Yuseok Bae, Jung Uk Kim +1Vision Foundation Model Adaptation Multi-Task Learning
Electronics and Telecommunications Research Institute (ETRI), Korea · Kyung Hee University, Korea · Chonnam National University, Korea
Parameter-Efficient Adaptation of SAM 3 for Automated ITV Generation from 4DCT Images
Jun 14, 2026Changwoo SongVision Foundation Model Adaptation Medical Image Segmentation
Oncosoft Inc., Seoul, South Korea · Department of Computer Science & Engineering, Chungnam National University, Daejeon, South Korea
How do Self-Supervised Remote Sensing Vision Models Transfer to Downstream Tasks?
Jun 11, 2026Julia Romero, Qin Lv, Morteza KarimzadehGeospatial Foundation Models Vision Foundation Model Adaptation
University of Colorado Boulder · Boulder, CO, USA
Adapting Prithvi-EO for Fallow Detection for Food-Water Nexus: ViT-Adapter Necks and Parameter-Efficient Backbone tuning of Geospatial Foundation Model
Jun 10, 2026Sk Muhammad Asif, Orhun AydinRemote Sensing Image Understanding Remote Sensing Small Object Detection
Earth, Atmospheric and Geospatial Science Saint Louis University Saint Louis, Missouri, USA
Don't waste SAM
Jun 9, 2026Nermeen Abou Baker, Uwe HandmannSupervised Fine-Tuning Promptable Image Segmentation
1- Ruhr West University of Applied Sciences - Dept of Computer Science Lutzowstrassse 5, 46236 Bottrop - Germany
Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning
Jun 8, 2026Ghodsiyeh Rostami, Po-Han Chen, Mahdi S. HosseiniFine-Tuning Vision Foundation Model Adaptation
Concordia University · Concordia University Mila - Québec AI Institute
GeoRoPE: Ground-Aware Rotary Adaptation for Remote Sensing Foundation Models
Jun 8, 2026Yu Luo, Kun Hu, Mengwei He +7Rotary Positional Embeddings Geospatial Foundation Models
The University of Sydney · Edith Cowan University · Adelaide University +3
Shift-Dependent Asymmetry: Orthogonal Inverse Low-Rank Adaptation for Federated Medical Segmentation
Jun 7, 2026Xingyue Zhao, Wenke Huang, Linghao Zhuang +7Vision Foundation Model Adaptation Non-IID Federated Learning
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · College of Computing and Data Science, Nanyang Technological University, Singapore +4
Self-Supervised Vision Transformers for CBCT-Based Detection of Temporomandibular Joint Osteoarthritis
Jun 6, 2026Shradhdha Trivedi, Vrundan Sojitra, Mariela PadillaMedical Imaging Foundation Models Cone-Beam CT
Herman Ostrow School of Dentistry, University of Southern California · Viterbi School of Engineering, University of Southern California
DRIFT: From Robustness Gaps to Invariance Manifolds for AI-Generated Image Detection
Jun 5, 2026Abhishek Ameta, Sayan Banerjee, Shreyas Pandith +4Vision Foundation Model Adaptation One-Class Classification
Samsung Research Institute, Bangalore, India
Multi-Task Crack Foundation Model for Engineering-Reliable Crack Representation and Topology Preservation in Civil Infrastructure
Jun 4, 2026Blessing Agyei Kyem, Joshua Kofi Asamoah, Eugene Denteh +1Vision Foundation Model Adaptation Topology-Preserving Image Segmentation
Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, 58102, ND, USA
Attention Consistent Longitudinal Medical Visual Question Answering Guided by Vision Foundation Models
Jun 3, 2026Jialin Wu, Qianru Zhang, Georges El Fakhri +1Vision Foundation Model Adaptation Medical VQA
Yale Biomedical Imaging Institute New Haven, CT 06510
Who Needs Labels? Adapting Vision Foundation Models With the Metadata You Already Have
Jun 3, 2026Elouan Gardès, Seung Eun Yi, Kartik Ahuja +6Unsupervised Domain Adaptation Domain Adaptation
Meta FAIR, Paris · Columbia University, New York · LIGM, CNRS, Gustave Eiffel, ENPC, IP Paris
Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation
Jun 3, 2026Qiaoyu Chen, Bing ZhangVision Foundation Model Adaptation AI-Generated Image Detection
Harbin University of Commerce
Geometry-Preserving Unsupervised Alignment for Heterogeneous Foundation Models
Jun 3, 2026Shuwen Yu, Zhanxuan Hu, Yi Zhao +2Cross-Modal Representation Learning Vision Foundation Model Adaptation
Yunnan Normal University, Kunming, China · Kunming University of Science and Technology, Kunming, China
Where Do We (Not) Need Temporal Context in Low-Resource Video Task Adaptation?
Jun 2, 2026Luc P. J. Sträter, Hazel DoughtyLinear Probing Vision Foundation Model Adaptation
Leiden University
Honey, I Shrunk the Arc de Triomphe!
Jun 1, 2026Yuanbo Xiangli, Hanyu Chen, Xueqing Tsang +13D Foundation Models Metric Depth Estimation
Cornell University · Shanghai Jiao Tong University
Beyond Low-Rank: Low-Rank Sparse Prompting via Spiking Neural Network and Prompt Factorization
Jun 1, 2026Yumiao Zhao, Bo Jiang, Beibei Wang +3Vision Foundation Model Adaptation Low-Rank Adaptation
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University
FlatVPR: Plug-and-play Geo-linear Residual Adapter for Geometric Rectification of Foundation Model Feature Manifolds
Jun 1, 2026Rai Hisada, Kanji TanakaVisual Place Recognition Vision Foundation Model Adaptation
Fundamental Engineering for Knowledge-Based Society, Graduate School of Engineering, University of Fukui, Japan.
SAM for Robust Mitochondria Instance Segmentation in Fluorescence Microscopy
May 29, 2026Suyog Jadhav, Dilip K. Prasad, Krishna AgarwalSynthetic Data Augmentation Instance Segmentation
UiT The Arctic University of Norway
TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation
May 29, 2026Abid Ali, Arunkumar Rathinam, Djamila AouadaVision Foundation Model Adaptation Object Pose Estimation
Clustering Guided Domain-Specific Pretrained Foundation Model for Very High-Resolution Arctic Remote Sensing
May 28, 2026Amal S. Perera, Chandi Witharana, Elias Manos +2Domain-Adaptive Pretraining Vision Foundation Model Adaptation
Department of Natural Resources and the Environment, University of Connecticut, Storrs, CT · Woodwell Climate Research Center, Falmouth, MA.
SLAD : Shared LoRA Adapters for Task Specific Distillation
May 28, 2026Reda Bensaid, Yassir Bendou, Vincent Gripon +1Vision Foundation Model Adaptation Adapter Tuning
IMT Atlantique, Brest, France · Polytechnique Montr´eal, Montr´eal, Canada
From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments
May 28, 2026Ji-Hoon Hwang, Jisung Bae, Dong-Wook Kim +2Vision Foundation Model Adaptation Robotic Perception

Jun 20, 2026, cs.CVJ/K move · Enter open · S save

Rethinking the Adaptation of Vision Foundation Models for Efficient Cell Segmentation

Qing Xu, Xiangjian He, Wenting Duan, Jiebo Luo+1

School of Computer Science, University of Nottingham, UK · School of Computer Science, University of Nottingham Ningbo China, China · School of Engineering and Physical Science, University of Lincoln, UK+2

Vision Foundation Model Adaptation Medical Image Segmentation

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Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remarkable universal feature representations, unlocking their full potential for cellular imaging is currently bottlenecked by resource-intensive adaptation paradigms. Existing methods typically rely on fine-tuning heavy visual encoders, leading to extensive computational overhead and a dependency on large-scale annotations. To address this, we propose the EffiCell-Seg framework for highly efficient cell segmentation without re-training the visual encoder. Our core insight is that pretrained VFMs intrinsically encode complementary structural priors: global saliency for localizing potential cells, and local morphological patterns for delineating cellular structures. To harness these priors, we devise a Cell Structure Prompt Encoder (CSP-Encoder) that synthesizes semantic-aware saliency and principal morphological features from frozen VFM representations into explicit structural prior maps. Moreover, we propose a Synergistic Mask Decoder (SM-Decoder) that enforces contextual consistency by jointly predicting geometric distance fields and semantic maps via mutual cross-guidance. Extensive experiments demonstrate that EffiCell-Seg outperforms state-of-the-art methods across diverse cell imaging modalities while requiring only ~5M trainable parameters, over 130x fewer than fully fine-tuned VFM counterparts. The code is available at https://github.com/xq141839/EffiCell-Seg.

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