Vision Foundation Model Adaptation

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JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery
Sep 7, 2026Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Jia Chen +1Remote Sensing Image Segmentation Agricultural Remote Sensing
University of California, Riverside
PL-SCEA: Reconfiguring Pretrained Attention for Few-Shot Industrial Anomaly Detection
Sep 3, 2026Xiaoyu Yang, Qixing Wu, Huixian Zhao +1Few-Shot Anomaly Detection Vision Foundation Model Adaptation
School of Airspace Science and Engineering, Shandong University, Shandong, China
SAM3-LoRA: Parameter-Efficient Adaptation of a Concept-Promptable Foundation Model for Multi-Class Structural Defect Segmentation
Aug 31, 2026P. Malaisree, S. Youwai, S. Janrungautai +3Promptable Image Segmentation Vision Foundation Model Adaptation
MAA Consultants Co., Ltd. · AI Research Group, Department of Civil Engineering, Faculty of Engineering, King Mongkut´s University of Technology Thonburi (KMUTT). · School of Engineering, University of Phayao. +2
GAFT: Geo-Anchored Fine-Tuning for Hazard Identification from Rare Failures
Aug 31, 2026Yanran Xu, Chuanhang Qiu, Yue Wang +2Robot Navigation Vision Foundation Model Adaptation
University of Southampton, Southampton, United Kingdom
Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection
Aug 30, 2026Hatef Otroshi Shahreza, Asif Hussain Khan, Peter Lorenz +2Image Forgery Detection Vision Foundation Model Adaptation
Idiap Research Institute, Switzerland · Université de Lausanne, Switzerland
GramLoop: Training-Free Gram-Gated Replay for Robust Dense Prediction
Aug 29, 2026Yang Chen, Canyu Shen, Xinzhe Rao +5Distribution Shift Robustness Test-Time Adaptation
Huazhong University of Science and Technology · Tongji University · King Abdullah University of Science and Technology +2
Subgroup performance analysis of adaptation strategies for chest X-ray foundation models
Aug 19, 2026Dhruv Gupta, Emma A. M. Stanley, Fabio De Sousa Ribeiro +2Chest X-Ray Classification Medical Imaging Foundation Models
Department of Computing, Imperial College London, UK · Causality in Healthcare AI Hub, UK · Department of Radiology, Royal Brompton Hospital, UK +1
Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery
Aug 11, 2026Roni Blushtein-Livnon, Tal Svoray, Osher Rafaeli +4Remote Sensing Image Segmentation Promptable Image Segmentation
Department of Environmental, Geoinformatics and Urban Planning Sciences, Ben-Gurion University of the Negev, Israel · Department of Psychology, Ben-Gurion University of the Negev, Israel · Department of Geography, The Hebrew University of Jerusalem, Isreal +1
Self-Geometry: GT-Free and Plug-and-Play Test-Time Adaptation for Geometrically Consistent 3D Vision Foundation Models
Aug 11, 2026Seokhyun Youn, Dahyeon Kye, Sung-Ho Bae +1Multi-View Consistency Multi-View Geometry
1CMLab, Chung-Ang University · 2Kyung Hee University
Retrieval-Augmented Vision Foundation Models for Robust Leukemia Cell Classification across Multiple Microscopy Datasets
Aug 11, 2026Carlos Zamora, Hiram Zuniga, Ulises Orozco-Rosas +1Retrieval-Augmented Classification Vision Foundation Model Adaptation
CETYS Universidad, Ave. CETYS Universidad No. 4, Fracc. El Lago, C.P. 22210, Tijuana, Baja California, Mexico
LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection
Aug 10, 2026Peter Lorenz, Anjith George, Marcel SébastienVision Foundation Models Vision Foundation Model Adaptation
Idiap Research Institute, Martigny, Switzerland · University of Lausanne (UNIL), Lausanne, Switzerland
GeoPhysAdapter: Scale-Matched Geophysical Adaptation for Cross-Domain Landslide Mapping with Vision Foundation Models
Aug 10, 2026Zhihang Liu, Mei-Po Kwan, Jinlin Wu +1Remote Sensing Image Segmentation Unsupervised Domain Adaptation
Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Shatin, 999077, Hong Kong SAR, China · Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, 999077, Hong Kong SAR, China · Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511400, Guangdong, China +1
Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation
Aug 10, 2026Xuanyu Liu, Zheng Fang, Hongyang He +2Mixture of Experts Vision Foundation Model Adaptation
Department of Computer Science The University of Warwick Coventry, UK · Manifolda.AI London, UK · Institute for Math & AI Wuhan University Wuhan, China
Circuit Fine-Tuning for Compute-Efficient Transformer Adaptation
Aug 8, 2026Uri Z. Kialy, Gil Ben-ArtziFine-Tuning Vision Foundation Model Adaptation
School of Computer Science, Ariel University, Israel
AdaDINO: Pair-Aware In-Backbone Adaptation of Frozen DINO for Efficient Remote Sensing Change Detection
Aug 8, 2026Xu Zhang, Xinqing Li, Jianpeng Xie +3Vision Foundation Model Adaptation Remote Sensing Change Detection
VCIP, College of Computer Science, Nankai University · School of Computer Science and Engineering, Tianjin University of Technology · Academy for Advanced Interdisciplinary Studies, Nankai University +1
LoRSA: Toward Generalizable Parameter-Efficient Fine-Tuning for Biomedical Downstream Tasks
Aug 7, 2026Saed Moradi, Benyamin Ghojogh, M. Hadi Sepanj +2Domain Generalization Vision Foundation Model Adaptation
Department of Oncology, McMaster University, Hamilton, Canada · Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada · Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Canada. +1
CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting
Aug 7, 2026Quang Minh Dinh, Tuan Kiet DoanLong-Horizon Video Generation Vision Foundation Model Adaptation
Simon Fraser University, Burnaby, BC, Canada · Institut Polytechnique de Paris, Palaiseau, France
Foundation Models Adaptation for Multi-View Multi-modal Cardiac MRI Segmentation and Direct Ejection Fraction Estimation
Aug 7, 2026Sina Amirrajab, Cian M Scannell, Volker Vehof +2Medical Imaging Foundation Models Left Ventricular Ejection Fraction Estimation
The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, the Netherlands. · Division of Cardiovascular Imaging, Department of Cardiology I, University Hospital Münster, Münster, Germany · Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Adapting Vision Foundation Models with Cascaded Semantics
Aug 5, 2026Xi Xiao, Xingjian Li, Cheng Han +8Vision Foundation Model Adaptation Visual Prompt Learning
University of Alabama at Birmingham · Carnegie Mellon University · University of Missouri–Kansas City +4
Foreseeing the Invisible: Amodal Reconstruction of Leaf Fossil Images
Aug 5, 2026Liuxiang Yue, Ailin Zhang, Ziyue Zhao +1Image Segmentation Image Reconstruction
Global College Shanghai Jiao Tong University Shanghai, China
PixelUp: Zero-Shot Semantic Feature Upsampling for Fine-Grained Vision Tasks
Aug 3, 2026Deepank Singh, Anurag Nihal, Vedhus HoskereDepth Estimation Vision Foundation Model Adaptation
University of Houston
MoRAL: Sensor-Grounded BEV Reasoning for Compact VLMs toward Edge-Oriented Autonomous Driving
Aug 3, 2026Ambarish Govindarajulu Kaliamurthi, Kaikai LiuVLMs for Autonomous Driving Efficient VLM Inference
San Jose State University · Computer Engineering Department · San José State University (SJSU) +1
GIFT: Geometry-Invariant Fine-Tuning for Non-Lambertian Monocular Depth Estimation
Aug 3, 2026Xianghui Fan, Zhaoyu Chen, Bingqian Wu +6Depth Estimation Monocular Depth Estimation
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences · University of Chinese Academy of Sciences · Fudan University +1
SPAE: Spectrally Guided Autoencoder for Pretrained Visual Latents
Aug 2, 2026Yibin Huang, Jixiang Hong, Zongzhao Li +8Autoencoders Vision Foundation Model Adaptation
Alibaba Group · Gaoling School of Artificial Intelligence, Renmin University of China
Learning the Pareto Frontier of Predictive Models under Distribution Shift
Aug 1, 2026Yiming Dong, Jiwei Zhao, Yang Young LuDistribution Shift Robustness Vision Foundation Model Adaptation
Department of Biostatistics & Medical Informatics, University of Wisconsin–Madison · Department of Statistics, University of Wisconsin–Madison · Department of Biomedical Engineering, University of Wisconsin–Madison
Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
Aug 1, 2026Mingya Alexa Gong, Da Ma, Lovre Antonio Budimir +7Medical Imaging Foundation Models Diabetic Retinopathy Grading
Institute of Ophthalmology, University College London, London, United Kingdom · Wake Forest University School of Medicine, Winston-Salem, NC, USA · Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Blacksburg, VA, USA +5
Training-Free Entity-Level Few-Shot Segmentation of Remote Sensing Images with Advection Refinement
Jul 31, 2026Xueting Bai, Huan NiRemote Sensing Image Segmentation Vision Foundation Model Adaptation
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Locally Consistent Transductive Information Maximization for Few-Shot Remote Sensing Scene Classification
Jul 31, 2026Karim El Khoury, Benoît Gérin, Benoît Macq +1Remote Sensing Image Understanding Vision Foundation Model Adaptation
ICTEAM, UCLouvain, Louvain-la-Neuve, Belgium
SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting
Jul 31, 2026Yu Song, Hao Sun, Shiyu Teng +2Image Segmentation Vision Foundation Model Adaptation
College of Information Science and Engineering, Ritsumeikan University 2-150 Iwakura-cho, Ibaraki, Osaka 567-8570, Japan
Step-Attention Refinement of DINOv3 Features for Efficient Anterior Eye Segmentation
Jul 29, 2026Philippe Baumstimler, Jean-Mathieu Gagnon, Sébastien Gagné +3Domain Generalization Vision Foundation Model Adaptation
Polytechnique Montréal, Montréal QC, Canada · LightX Innovations, Montréal QC, Canada · Institut de l’Oeil des Laurentides, Boisbriand QC, Canada

Sep 7, 2026, cs.CVJ/K move · Enter open · S save

JEDI: JEPA-to-Edge Distillation for Efficient Cropland Segmentation from Satellite Imagery

Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Jia Chen, Evangelos E. Papalexakis

University of California, Riverside

Remote Sensing Image Segmentation Agricultural Remote Sensing Edge Inference Vision Foundation Model Adaptation Knowledge Distillation

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Large vision models provide useful representations for remote-sensing segmentation but are often too expensive for deployment at the satellite or field edge. Existing feature-level distillation methods also tend to assume similar teacher and student architectures and often stop feature alignment when task training begins. We introduce JEDI (JEPA-to-Edge Distillation), a two-stage framework that transfers representations from a large I-JEPA Vision Transformer teacher to a compact SegFormer student. First, JEDI aligns the student's terminal representation with the teacher's token space using cross-architecture projection and spatial alignment. It then jointly optimizes supervised segmentation, temperature-scaled response distillation, and persistent feature alignment throughout task adaptation. On CalCROP21, JEDI-B0 achieves 68.0 mean Intersection-over-Union (mIoU) with 4.04M parameters, improving over the standalone student by 16.0 points and coming within 2.0 points of the 70.0 mIoU achieved by the 639M-parameter teacher. We evaluate SegFormer B0, B1, and B2 students with 4.04M, 14.33M, and 28M parameters, respectively. Across all three variants, JEDI consistently outperforms response-, structure-, channel-, and relational-distillation baselines under the same teacher-student setting. These results show that persistent representation alignment is especially valuable under aggressive compression, substantially reducing model size and computation while preserving segmentation performance.

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