Hyperspectral image (HSI) classification is a significant research area in remote sensing with a wide range of application scenarios. Recently, numerous HSI classification methods based on convolutional neural networks (CNNs) and Transformers have demonstrated promising classification performance. However, these methods demonstrate insufficient capability in mining spectral–spatial relationships from limited HSI samples and fail to extract features pertinent to the target category. To address these challenges, we propose a multi-scale adaptive diffusion feature fusion and causal invariance learning (MSDF-CIL) framework based on diffusion models. Specifically, the framework establishes spectral–spatial distribution relationships through forward and backward diffusion processes. The forward process gradually introduces noise to the HSI input. In the backward diffusion process, our pre-trained hyperspectral denoising network extracts semantically rich multi-scale diffusion features from complex spectral–spatial relationships. A multi-scale adaptive diffusion feature fusion (MSADFF) module is designed to learn key information about each scale and fuse it to enhance the representation. In addition, a causal invariance learning (CIL) module is designed to focus on features causally related to the target class, enabling the model to eliminate spurious correlations among diffusion features. Experimental results on three public HSI datasets show that the proposed MSDF-CIL outperforms other state-of-the-art HSI classification methods, even with minimal samples. When the number of training samples in each class reaches 30, the MSDF-CIL achieves overall accuracy improvements of 4.0% on the Pavia University dataset, 2.3% on the Indian Pines dataset, and 2.3% on the Houston13 dataset, respectively.

The task of scene text recognition involves processing information from two modalities: images and text, thereby requiring models to have the ability to extract features from images and model sequences simultaneously. Although linguistic knowledge greatly aids scene text recognition tasks, the extensive use of language models in sequence modeling and model prediction stages in recent years has made model architectures increasingly complex and inefficient. In this paper, we propose LCSTR, a pure convolutional visual model that can complete text recognition without the need for attention mechanisms or language models. This approach applies large kernels to text recognition tasks for the first time, extracting word-level text information through large text-aware blocks, capturing long-range dependencies between characters, and using small text-aware blocks to obtain local features within characters. Experiments show that this model strikes a good trade-off between accuracy and speed, achieving notable results on seven public benchmarks, validating the generalizability and effectiveness of this method. Furthermore, owing to the absence of a language module, this model demonstrates remarkable accuracy even in limited sample scenarios, and the lightweight and low computational overhead features make it suitable for engineering applications.

Nowadays, data in the real world often comes from multiple sources, but most existing multi-view ${K}$ -Means perform poorly on linearly non-separable data and require initializing the cluster centers and calculating the mean, which causes the results to be unstable and sensitive to outliers. This paper proposes an efficient multi-view ${K}$ -Means to solve the above-mentioned issues. Specifically, our model avoids the initialization and computation of clusters centroid of data. Additionally, our model use the Butterworth filters function to transform the adjacency matrix into a distance matrix, which makes the model is capable of handling linearly inseparable data and insensitive to outliers. To exploit the consistency and complementarity across multiple views, our model constructs a third tensor composed of discrete index matrices of different views and minimizes the tensor's rank by tensor Schatten ${p}$ -norm. Experiments on two artificial datasets verify the superiority of our model on linearly inseparable data, and experiments on several benchmark datasets illustrate the performance.

Recently, multi-view clustering methods have been widely used in handling multi-media data and have achieved impressive performances. Among the many multi-view clustering methods, anchor graph-based multi-view clustering has been proven to be highly efficient for large-scale data processing. However, most existing anchor graph-based clustering methods necessitate post-processing to obtain clustering labels and are unable to effectively utilize the information within anchor graphs. To address this issue, we draw inspiration from regression and feature selection to proposeAnchorGraph-BasedFeatureSelection forOne-stepMulti-ViewClustering (AGFS-OMVC). Our method combines embedding learning and sparse constraint to perform feature selection, allowing us to remove noisy anchor points and redundant connections in the anchor graph. This results in a clean anchor graph that can be projected into the label space, enabling us to obtain clustering labels in a single step without post-processing. Lastly, we employ the tensor Schatten$p$-norm as a tensor rank approximation function to capture the complementary information between different views, ensuring similarity between cluster assignment matrices. Experimental results on five real-world datasets demonstrate that our proposed method outperforms state-of-the-art approaches.