Vision Foundation Model Adaptation

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GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection
May 28, 2026Jiacong Liu, Shu Luo, Yikai Qin +3Synthetic Data Augmentation Few-Shot Domain Adaptation
Huazhong University of Science and Technology
SIGMA: Bridging Structural and Distributional Gaps for Vision Foundation Model Adaptation
May 27, 2026Lingyu Xiong, Jinjin Shi, Xuran Xu +3Vision Foundation Model Adaptation Dense Prediction
Xiaomi Corporation
RepSAM: Bridging Foundation Models to Robotic Vision via Representation-Guided Adaptation
May 25, 2026Wenhui ChuVision Foundation Model Adaptation Robotic Manipulation
Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
Self-Supervised Contrastive Learning for Cardiac MR Sequence Classification
May 24, 2026Yuli Wang, Hyewon Jung, Dongshen Peng +10Vision Transformer Vision Foundation Model Adaptation
Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA · Department of Electrical and Computer Engineering, Johns Hopkins University,2026 Baltimore, MD, 21218, USA · Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 +4
Rethinking Transfer Learning for Industrial Inspection: DINOv3 vs. ImageNet Pretraining Across RGB and X-ray Tasks
May 22, 2026Mehdi Gharbage, Céline Teulière, Pierre Bouges +1Self-Supervised Pre-Training Industrial Anomaly Detection
1Michelin Tyres Manufacturer · 2Universit´e Clermont Auvergne, CNRS, Institut Pascal, Clermont-Ferrand, France
Enhancing Gaze Reasoning in Vision Foundation Models for Gaze Following
May 21, 2026Shijing Wang, Yaping Huang, Chaoqun Cui +4Vision Foundation Model Adaptation
Beijing Jiaotong University · MAIS, Institute of Automation, Chinese Academy of Sciences · Microsoft, UK +1
Rethinking Noise-Robust Training for Frozen Vision Foundation Models: A Cross-Dataset Benchmark with a Case Study of Small-Loss Failure
May 21, 2026Zitong Li, Haoyu WangVision Foundation Model Adaptation Noisy-Label Learning
Department of Biostatistics and Health Informatics · King's College London
Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking
May 21, 2026Deyi Zhu, Yuji Wang, Yong Liu +4Visual Object Tracking Vision Foundation Model Adaptation
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Department of Automation, Tsinghua University, Beijing 100084, China
Accelerating Vision Foundation Models with Drop-in Depthwise Convolution
May 21, 2026Carmelo Scribano, Mohammad Mahdi, Nedyalko Prisadnikov +5Vision Foundation Models Vision Foundation Model Adaptation
University of Modena and Reggio Emilia, Modena, Italy. · INSAIT, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria
Lighting-aware Unified Model for Instance Segmentation
May 19, 2026Qisai Liu, Alloy Das, Zhanhong Jiang +4Instance Segmentation Vision Foundation Model Adaptation
Iowa State University, Ames, IA 50011, USA
UniRefiner: Teaching Pre-trained ViTs to Self-Dispose Dross via Contrastive Register
May 19, 2026Congpei Qiu, Zhaoyu Hu, Wei Ke +3Vision Transformer Vision Foundation Model Adaptation
Xi’an Jiaotong University, School of Software Engineering · Harbin Institute of Technology (Shenzhen) · Shenzhen Loop Area Institute +1
Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation
May 19, 2026Junyuan Ma, Xunzhi Xiang, Wenbin Li +2Vision Foundation Model Adaptation Few-Shot Learning
1Nanjing University
Towards Data-Efficient Video Pre-training with Frozen Image Foundation Models
May 18, 2026Svetlana Orlova, Niccolò Cavagnero, Gijs DubbelmanStreaming Video Understanding Vision Foundation Model Adaptation
Eindhoven University of Technology
MedFM-Robust: Benchmarking Robustness of Medical Foundation Models
May 18, 2026Xiangxiang Cui, Tianjin Huang, Yifang Wang +2Multimodal Robustness Neural Network Robustness
Beijing Normal University, China · University of Exeter, United Kingdom · University College London, United Kingdom +2
Unleashing Vision Transformer Potential In Image Quality Assessment via Global-Local Adaptive Interaction
May 18, 2026Yu Li, Puchao Zhou, Yachun Mi +3Vision Transformer Vision Foundation Model Adaptation
Harbin Institute of Technology · Meituan
Weighted Reverse Convolution for Feature Upsampling
May 17, 2026Wentong Li, Zhiyuan Qi, Zichen Zhao +2Vision Foundation Model Adaptation Inverse Problems
Nanjing University of Aeronautics and Astronautics · The Hong Kong Polytechnic University · Nanjing University
HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone
May 17, 2026Guanyiman Fu, Jingtao Li, Zihang Cheng +8Vision Foundation Model Adaptation Hyperspectral Imaging
Griffith University, Australia · Wuhan University, China · Nanjing University of Science and Technology, China +2
LoCO: Low-rank Compositional Rotation Fine-tuning
May 15, 2026An Nguyen, Jaesik Choi, Anh TongFine-Tuning LLM Fine-Tuning
1Korea University · 2KAIST · 3INEEJI
GAP: Geometric Anchor Pre-training for Data-Efficient Visuomotor Learning of Manipulation Tasks
May 15, 2026Davide Buoso, Andrea Protopapa, Stefano Di Carlo +2Vision Foundation Model Adaptation Visuomotor Policy Learning
Department of Control and Computer Engineering, Polytechnic University of Turin, Italy
Weakly Supervised Segmentation as Semantic-Based Regularization
May 13, 2026Stefano Colamonaco, Andrei-Bogdan Florea, Jaron MaeneImage Segmentation Weakly Supervised Image Segmentation
KU Leuven · Apixa
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
May 12, 2026Muhammad Aqeel, Maham Nazir, Uzair Khan +2VLM Adaptation Medical Image Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy · School of Computer Science and Engineering, Beihang University, China · Dept. of Engineering for Innovation +1
Vision Transformer-Conditioned UNet for Domain-Adaptive Semantic Segmentation
May 12, 2026Joel Valdivia Ortega, Tingying Peng, Marion JasninHybrid CNN-Transformer Architectures Vision Foundation Model Adaptation
Helmholtz AI, Helmoltz Munich, Neuherberg, Germany · Helmholtz Pioneer Campus, Helmholtz Munich, Neuherberg, Germany · School of Computation, Information and Technology, TUM, Garching, Germany +1
BGG: Bridging the Geometric Gap between Cross-View images by Vision Foundation Model Adaptation for Geo-Localization
May 11, 2026Wei Wang, Dou Quan, Ning Huyan +4Cross-View Geo-Localization Geospatial Representation Learning
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University · Department of Automation, Tsinghua University, Beijing 100084, China · School of Telecommunications, Xidian University, Xi’an 710071, China
AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splatting
May 11, 2026Mingwei Xing, Xinliang Wang, Yifeng Shi3D Gaussian Splatting Vision Foundation Model Adaptation
Ke Holdings Inc.
Frequency Adapter with SAM for Generalized Medical Image Segmentation
May 11, 2026Phuoc-Nguyen Bui, Van-Nguyen Pham, Duc-Tai Le +2Frequency-Domain Feature Learning Domain Generalization
Sungkyunkwan University, Korea
SSDA: Bridging Spectral and Structural Gaps via Dual Adaptation for Vision-Based Time Series Forecasting
May 10, 2026Mingrui Zhang, Hanchen Yang, Wengen Li +4Domain Adaptation Time Series Forecasting
MC-RFM: Geometry-Aware Few-Shot Adaptation via Mixed-Curvature Riemannian Flow Matching
May 8, 2026Salim Khazem, Ibrahim Mohamed Serouis, Zakaria EzzahedFlow Matching Vision Foundation Model Adaptation
Talan Research Center, Paris, France
Prompt-Free and Efficient SAM2 Adaptation for Biomedical Semantic Segmentation via Dual Adapters
May 7, 2026Hinako Mitsuoka, Kazuhiro HottaPromptable Image Segmentation Vision Foundation Model Adaptation
Meijo University, 1-501 Shiogamaguchi, Tempaku-ku, Nagoya, 468-8502, Japan
EGA: Adapting Frozen Encoders for Vector Search with Bounded Out-of-Distribution Degradation
May 7, 2026Dongfang ZhaoOOD Generalization Vision Foundation Model Adaptation
Tacoma School of Engineering and Technology University of Washington
Deep Reprogramming Distillation for Medical Foundation Models
May 6, 2026Siyuan Du, Yuhang Zhou, Haolin Li +5Medical Imaging Foundation Models Vision Foundation Model Adaptation
School of Computer Science, Fudan University and with the Shanghai AI Laboratory, Shanghai, China · Shanghai AI Laboratory, Shanghai, China · Cooperative Medianet Innovation Center, Shanghai Jiao Tong University and with the Shanghai AI Laboratory, Shanghai, China +3

May 28, 2026, cs.CVJ/K move · Enter open · S save

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

Jiacong Liu, Shu Luo, Yikai Qin, Yaze Zhao+2

Huazhong University of Science and Technology

Synthetic Data Augmentation Few-Shot Domain Adaptation Vision Foundation Model Adaptation Few-Shot Object Detection Few-Shot Learning Pseudo-Labeling+1

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Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they face two critical challenges in fine-tuning: insufficient support set utilization due to sparse single-instance annotations, and severe overfitting under extremely limited target-domain samples. To address these issues, this paper proposes GiPL, an efficient two-branch training framework. In the first branch, we design an iterative pseudo-label self-training paradigm, which performs zero-shot inference on the support set to generate reliable pseudo-annotations, fuses them with ground-truth labels, and iteratively optimizes the model to fully exploit support set data. In the second branch, we introduce generative data augmentation pipeline using large vision-language models, which synthesizes domain-aligned, multi-object annotated images to enrich training samples and suppress overfitting. Extensive experiments on three challenging CD-FSOD datasets (RUOD, CARPK, CarDD) under 1/5/10-shot settings demonstrate that GiPL consistently outperforms state-of-the-art methods with significant performance gains. Code is available at CDiscover.

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