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cs.CV2024
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier
Aristeidis Tsaris, Chengming Zhang, Xiao Wang +9
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence length…
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…