Multimodal remote sensing imagery classification (MRSIC) aims to synergistically leverage complementary information from heterogeneous data sources, enabling precise land-cover classification. Existing MRSIC approaches predominantly rely on abundant annotated samples, facing critical performance degradation under data-scarce scenarios that are particularly exacerbated by the inherent complexity of heterogeneous multimodal data. Furthermore, effectively extracting spatial-spectral information of multimodal data and fusing the cross-modal heterogeneous features persists as a significant challenge. To address these obstacles, we propose a pioneering few-shot MRSIC network, Two-timer-KAN, which integrates modality-specific feature extraction for spectral- and spatial-dominant data. Specifically, leveraging the nonlinear power of Kolmogorov-Arnold Networks (KANs), we develop the Dual-Exclusive Fourier KAN (DEF-KAN) encoder, which captures modality-specific global features in the frequency domain, bridging spectral and spatial gaps across various datasets. Following this, a Multivariate-Gaussian-based Cross-KAN (MG-Cross-KAN) is dedicated to enhancing the robustness of cross-modality fusion by capturing modality-shared features in a distribution-based manner. Finally, to further tackle classification ambiguity under limited annotated samples, we present a visual-textual bidirectional alignment strategy, which leverages textual descriptions as supplementary semantical knowledge to clarify class feature centers. Extensive experiments demonstrate that the proposed two-timer-KAN achieves superior performance, outperforming the state-of-the-art methods in both accuracy and robustness.

Recently, domain alignment and metric-based few-shot learning (FSL) have been introduced into hyperspectral image classification (HSIC) to solve the issues of uneven data distribution and scarcity of annotated data faced in practical applications. However, existing cross-domain few-shot methods ignore pivotal frequency priors of the complex field, which contribute to better category discrimination and knowledge transfer. To address this issue, we propose a novel physics-guided time-interactive-frequency network (PTFNet) for cross-domain few-shot HSIC, enabling the extraction of both frequency priors and spatial features (termed "time domain" following Fourier convention) simultaneously through a lightweight time-interactive-frequency module (TiF-Module) as a pioneering effort. Meanwhile, a spectral Fourier-based augmentation module (SFA-Module) is designed to decouple the frequency priors and enhance the diversity of distribution of physical attributes to imitate the domain shift. Then, the physics consistency loss is introduced to regularize the diverse embeddings to approximate the center of each category's physical attributes, guiding the network to excavate more transferable knowledge of source domain (SD). Furthermore, to fully exploit the discriminant time-frequency information and further improve the accuracy of boundary pixels, a set of multiorientation homogeneous prototypes is adopted to represent each class comprehensively, and an intuitive and flexible uncertainty-rectified bidirectional random walk strategy is applied to replace the Euclidean metric for more reliable classification. The experimental results on four public datasets demonstrate the prominent performance of the proposed PTFNet.

Recommender System plays an important role in keeping people engaged with online services, and collaborative filtering is a main technique for recommendation. With the immense influence of deep learning, there is a growing interest in applying it to collaborative filtering. Existing methods have applied different ways to learn the user-item interaction function, however, most of these methods have limitation in modeling user-item correlations because they ignore the original user-item information and the large size of embeddings. In this work we propose Stacked Embedding Convolutional Neural Collaborative Filtering (SECNCF), a novel neural collaborative filtering architecture. The idea is to create a pedrail by stacking embeddings which are composed of user embedding, item embedding and latent factors. We apply convolutional neural network (CNN) above the pedrail layer to capture the local features of dimension correlations. This method is good at extracting rich local dimension correlations of embeddings and is scalable for modeling user-item interactions. Extensive experiments on three public accessible datasets show that our method makes significant improvement over the state-of-the-art methods.