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Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation
Specifically, we first propose a Region Mask Contrastive (RMC) loss and a Region Feature Contrastive (RFC) loss to accomplish region-level contrastive property.
W-net: Simultaneous segmentation of multi-anatomical retinal structures using a multi-task deep neural network
To further prove the generalization property of the proposed method, we applied the trained model on the DRIONS-DB dataset for OD segmentation and on the MESSIDOR dataset for exudate segmentation.
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters.
"Tom" pet robot applied to urban autism
The consequent urban autism problem has become more and more serious.
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