Masked Autoencoders

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13 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware
Feb 18, 2026David Smerkous, Zian Wang, Behzad NajafianMasked Autoencoders Self-Supervised Pre-Training
Department of Laboratory Medicine & Pathology, University of Washington, Seattle WA, USA · Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA · Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle WA, USA
Stochastic Siamese MAE Pretraining for Longitudinal Medical Images
Dec 29, 2025Taha Emre, Arunava Chakravarty, Thomas Pinetz +9Masked Autoencoders Self-Supervised Pre-Training
Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Austria · Department of Ophthalmology and Optometry, Medical University of Vienna, Austria · BioMedIA, Department of Computing, Imperial College London, London, United Kingdom +8
Scaling Vision Transformers for Functional MRI with Flat Maps
Oct 15, 2025Connor Lane, Mihir Tripathy, Leema Krishna Murali +15Medical Imaging Foundation Models Masked Autoencoders
MedARC · Sophont · Baylor College of Medicine +3
Occlusion-Aware 3D Hand-Object Pose Estimation with Masked AutoEncoders
Jun 12, 2025Hui Yang, Wei Sun, Jian Liu +3Masked Autoencoders 3D Hand Pose Estimation
National Engineering Research Center for Robot Visual Perception and Control Technology, School of Artificial Intelligence and Robotics, Hunan University, Changsha, 410082, China · School of Architecture and Art, Central South University, Changsha, 410082, China · Department of Computer Science and Software Engineering, The University of Western Australia, WA 6009, Australia

Feb 18, 2026, cs.CVJ/K move · Enter open · S save

AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware

David Smerkous, Zian Wang, Behzad Najafian

Department of Laboratory Medicine & Pathology, University of Washington, Seattle WA, USA · Electrical Engineering & Computer Science, Oregon State University, Corvallis, OR, USA · Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle WA, USA

Masked Autoencoders Self-Supervised Pre-Training Efficient ViTs Microscopy Image Analysis Visual Token Compression

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Self-supervised pretraining has transformed computer vision by enabling data-efficient fine-tuning, yet high-resolution pretraining typically requires server-scale infrastructure, limiting custom in-domain training for many research laboratories. Masked Autoencoders (MAE) reduce computation by encoding only visible tokens, but combining MAE with hierarchical downsampling architectures has remained structurally challenging due to dense grid priors and mask-aware design compromises. We introduce AFFMAE, a masking-friendly hierarchical pretraining framework built on adaptive, off-grid token merging. AFFMAE removes dense-grid assumptions while preserving hierarchical scalability during pre-training and fine-tuning. To support this architecture, we developed numerically stable mixed-precision Triton kernels and a lightweight, point-based decoder that can be directly repurposed as a segmentation head. On high-resolution microscopy segmentation, AFFMAE matches MAE finetuning performance on foot process width estimation with ViT backbone at equal parameter counts while being 2x faster during pre-training and halving peak memory usage. Furthermore, AFFMAE achieves up to 5x throughput speedups fine-tuning at the 1024px resolution, providing high-resolution model training on desktop hardware. Code available at https://github.com/najafian-lab/affmae.

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Latest papers 64

AFFMAE: Scalable Vision Pre-Training for High-Resolution Microscopy Segmentation on Desktop Hardware

Stochastic Siamese MAE Pretraining for Longitudinal Medical Images

Scaling Vision Transformers for Functional MRI with Flat Maps

Occlusion-Aware 3D Hand-Object Pose Estimation with Masked AutoEncoders