An attention mechanism assigns different weights to different features to help a model select the features most valuable for accurate classification. However, the traditional attention mechanism algorithm often allocates weights in a one-way fashion, which can result in a loss of feature information. To obtain better hyperspectral data classification results, a novel cross-attention mechanism and graph convolution integration algorithm are proposed in this letter. First, principal component analysis is used to reduce the dimensionality of hyperspectral images to obtain low-dimensional features that are more expressive. Second, the model uses a cross (horizontal and vertical directions) attention algorithm to allocate weights jointly based on its two strategies; then, it adopts a graph convolution algorithm to generate the directional relationships between the features. Finally, the generated deep features and the relationship between the deep features are used to complete the prediction of hyperspectral data. Experiments on three well-known hyperspectral data sets--Indian Pines, the University of Pavia, and Salinas--show that the proposed algorithm achieves better performances than do other well-known algorithms using different methods of training set division.

The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image. But this type of method generally attempts to generate one single "optimal" result, ignoring many other plausible results. Considering the uncertainty of the inpainting task, one sole result can hardly be regarded as a desired regeneration of the missing area. In view of this weakness, which is related to the design of the previous algorithms, we propose a novel deep generative model equipped with a brand new style extractor which can extract the style feature (latent vector) from the ground truth. Once obtained, the extracted style feature and the ground truth are both input into the generator. We also craft a consistency loss that guides the generated image to approximate the ground truth. After iterations, our generator is able to learn the mapping of styles corresponding to multiple sets of vectors. The proposed model can generate a large number of results consistent with the context semantics of the image. Moreover, we evaluated the effectiveness of our model on three datasets, i.e., CelebA, PlantVillage, and MauFlex. Compared to state-of-the-art inpainting methods, this model is able to offer desirable inpainting results with both better quality and higher diversity. The code and model will be made available on https://github.com/vivitsai/PiiGAN.

E-commerce offers various merchandise for selling and purchasing with frequent transactions and commodity flows. An accurate prediction of customer needs and optimized allocation of goods is required for cost reduction. The existing solutions have significant errors and are unsuitable for addressing warehouse needs and allocation. That is why businesses cannot respond to customer demands promptly, as they need accurate and reliable demand forecasting. Therefore, this paper proposes spatial feature fusion and grouping strategies based on multimodal data and builds a neural network prediction model for e-commodity demand. The designed model extracts order sequence features, consumer emotional features, and facial value features from multimodal data from e-commerce products. Then, a bidirectional long short-term memory network- (BiLSTM-) based grouping strategy is proposed. The proposed strategy fully learns the contextual semantics of time series data while reducing the influence of other features on the groups local features. The output features of multimodal data are highly spatially correlated, and this paper employs the spatial dimension fusion strategy for feature fusion. This strategy effectively obtains the deep spatial relations among multimodal data by integrating the features of each column in each group across spatial dimensions. Finally, the proposed models prediction effect is tested using e-commerce dataset. The experimental results demonstrate the proposed algorithms effectiveness and superiority.

Since each sample in a hyperspectral remote sensing image is made up of high-dimensional features and contains a wealth of remote sensing features, feature selection and mining become more difficult. To address this issue, a multi-attention residual integrated network (MARB-Net) algorithm is proposed, which reduces redundant features while increasing feature fusion and, as a result, improves hyperspectral image recognition. First, assign multiple weights to each feature using multiple attention mechanism models; then, deep mine and integrate the features using the residual network; and finally, perform contextual semantic integration on the deep fusion features using the Bi-LSTM network. The recognition task should be completed by the Softmax classifier. The experimental results on three multi-class public data sets show that the MARB-Net algorithm proposed in this paper is effective.