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cs.CV2026
Scalable In-Domain Self-Supervised Foundation Model for Dense Representation Transfer in High-Resolution Plant Imaging
Junlin Guo, Sharmin Majumder, Isaac Lyngaas +2
High-resolution plant imaging enables detailed characterization of plant morphology, but dense scientific analysis remains limited by costly pixel-level annotations, large image pi…
cs.CV2026
Structure-Guided Masked Autoencoders for Ultra-High Resolution Scientific Image Understanding
Enzhi Zhang, Du Wu, Rui Zhong +18
Self-supervised pre-training with Vision Transformers, including Masked Autoencoders (MAE), is difficult to apply to gigapixel scientific images. Random masking is poorly matched t…
cs.CV2024
Adaptive Patching for High-resolution Image Segmentation with Transformers
Enzhi Zhang, Isaac Lyngaas, Peng Chen +5
Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the ima…