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Lightweight Multiscale Feature Fusion Super-Resolution Network Based on Two-branch Convolution and Transformer
Based on this, this paper proposes a new lightweight multi-scale feature fusion network model based on two-way complementary convolutional and Transformer, which integrates the respective features of Transformer and convolutional neural networks through a two-branch network architecture, to realize the mutual fusion of global and local information.
Compare learning: bi-attention network for few-shot learning
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only.
Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification
The results show that the proposed multitask SVM classification model based on 1-2gram TF-IDF features exhibits the best performance among the tested models.
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