ENHANCING REMOTE SENSING SEMANTIC SEGMENTATION ACCURACY AND EFFICIENCY THROUGH TRANSFORMER AND KNOWLEDGE DISTILLATION

Enhancing Remote Sensing Semantic Segmentation Accuracy and Efficiency Through Transformer and Knowledge Distillation

In semantic segmentation tasks, the transition from convolutional neural networks (CNNs) to transformers is driven by the latter's superior ability to capture global semantic information in remote sensing images.However, most transformer methods face challenges such as slow inference speed and limitations in capturing local features.To addre

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Sleep, eating disorder symptoms, and daytime functioning

Marilou DP Tromp,1 Anouk AMT Donners,1 Johan Garssen,1,2 Transistor Joris C Verster1,31Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, the Netherlands; 2Nutricia Research, Utrecht, the Netherlands; 3Center for Human Psychopharmacology, Swinburne University, Melbourne, VIC, AustraliaObjective: To

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