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In the field of hyperspectral image (HSI) classification, a source dataset with ample labeled samples is commonly utilized to enhance the classification performance of a target dataset with few labeled samples. Existing few-shot learning (FSL) ...

• A novel multi-branch feature extraction and fusion module.
• Featurewise transformation for features diversity and model generalization.
• Effective domain adaptation strategy for hyperspectral image few-shot classification.
• The ...

Deep learning methods have significantly progressed in hyperspectral image (HSI) classification. However, deep learning relies on large labeled data for training. The cost of labeling samples is enormous. In practical classification tasks, only a ...

Hyperspectral imaging is a highly advanced and sophisticated method for capturing images in hundreds of narrow, contiguous spectral bands. However, processing and analyzing such large amounts of data are challenging. Deep learning algorithms, ...

Aspect sentiment classification is an important branch of sentiment classification that has gained increasing attention recently. Existing aspect sentiment classification methods typically use different network branches to encode context and ...

Convolutional Neural Network(CNN) has been widely employed in hyperspectral image(HSI) classification. However, CNN cannot attain the relative location relation of spatial information well, hindering the further improvement of classification ...

The convolution neural network (CNN) methods have achieved excellent performance in hyperspectral image (HSI) classification. However, the convolution network fails to utilize the relative position information of the image effectively. The ...

Sentence pair modeling is a fundamental yet challenging issue for feature mining in natural language processing (NLP) tasks. Recently, most works have generated feature and sentence representation based on the interactive attention ...

In recent years, Convolutional Neural Networks (CNNs) have succeeded in Hyperspectral Image Classification and shown excellent performance. However, the implicit spatial information between features, which significantly affect the classification ...

In text classification models based on deep learning, feature extraction and feature aggregation are two key steps. As one of the basic feature extraction methods, CNN has certain limitations due to its inability to effectively extract ...