Low-light remote sensing images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space. This continuity in scenes leads to extensive long-range correlations in spatial domains within remote sensing images. Convolutional Neural Networks, which rely on local correlations for long-distance modeling, struggle to establish long-range correlations in such images. On the other hand, transformer-based methods that focus on global information face high computational complexities when processing high-resolution remote sensing images. From another perspective, Fourier transform can compute global information without introducing a large number of parameters, enabling the network to more efficiently capture the overall image structure and establish long-range correlations. Therefore, we propose a Dual-Domain Feature Fusion Network (DFFN) for low-light remote sensing image enhancement. Specifically, this challenging task of low-light enhancement is divided into two more manageable sub-tasks: the first phase learns amplitude information to restore image brightness, and the second phase learns phase information to refine details. To facilitate information exchange between the two phases, we designed an information fusion affine block that combines data from different phases and scales. Additionally, we have constructed two dark light remote sensing datasets to address the current lack of datasets in dark light remote sensing image enhancement. Extensive evaluations show that our method outperforms existing state-of-the-art methods. The code is available at https://github.com/iijjlk/DFFN.

Partial label learning (PLL) studies the problem of learning instance classification with a set of candidate labels and only one is correct. While recent works have demonstrated that the Vision Transformer (ViT) has achieved good results when training from clean data, its applications to PLL remain limited and challenging. To address this issue, we rethink the relationship between instances and object queries to propose K-means cross-attention transformer for PLL (KMT-PLL), which can continuously learn cluster centers and be used for downstream disambiguation tasks. More specifically, K-means cross-attention as a clustering process can effectively learn the cluster centers to represent label classes. The purpose of this operation is to make the similarity between instances and labels measurable, which can effectively detect noise labels. Furthermore, we propose a new corrected cross entropy formulation, which can assign weights to candidate labels according to the instance-to-label relevance to guide the training of the instance classifier. As the training goes on, the ground-truth label is progressively identified, and the refined labels and cluster centers in turn help to improve the classifier. Simulation results demonstrate the advantage of the KMT-PLL and its suitability for PLL.

Handling missing attribute values is a common challenge in data classification, often leading to degraded classification accuracy due to the reliance of most data-driven models on complete datasets. In practice, missing value imputation techniques are typically employed to obtain complete data for model construction; however, the quality of imputed values significantly influences the performance of downstream classification tasks, necessitating their improvement to achieve higher accuracy. To address this issue, we propose a novel method, incomplete data classification based on distribution alignment with evidence combination (IDAC), which is designed for scenarios with complete training data but incomplete test data. In IDAC, the missing attribute values of test data are initially imputed using the mean value vectors of the complete training data, minimizing the first-order distribution differences. The second-order statistical distributions (i.e., covariance matrices) of the training data and the imputed test data are subsequently aligned to derive two feature transformation matrices, which are used to map the complete training data and the incomplete test data into new feature representations. For classification, two classifiers are trained using the original and transformed feature representations of the training data, and their outputs are combined using evidence theory, with optimal combination weights determined by minimizing the mean squared error (MSE) between the combined results and the ground truth on the labeled training data. The proposed IDAC method was evaluated on multiple benchmark datasets, and experimental results demonstrate that IDAC consistently achieves superior classification performance compared to several advanced methods.

Partial label learning (PLL) is a paradigm in weakly supervised learning. The goal is to identify the ground-truth label from a set of candidate labels associated with a given sample. However, due to the ambiguity of labels, improving the accuracy of ground-truth label recognition is a challenge. In this paper, we propose an innovative training framework PLMR, for solving the PLL problem. Given the specificity of the PLL problem, its data is rich in valid information but significantly noisy. To overcome the problem, PLMR incorporates mutual information (MI) theory to mine the potential information of candidate labels and data features to distinguish between positive and negative sample pairs. This operation is to effectively utilize the raw data information in PLL and reduce the class conflict problem. In this way, the discriminative power of representation is improved according to the positive and negative pair selection strategy. At the same time, PLMR introduces cluster centers to optimize subsequent tasks, combining data augmentation samples with the K-means cross-attention mechanism to refine the optimized cluster centers. This is to improve the ability of the clustering centre to accurately represent the class information, thus improving the overall quality of the clusters. Further, through the ambiguity marking correction mechanism, weights are calculated based on the association between cluster centers and sample representations to guide model training. Experimental results show that PLMR demonstrates excellent classification performance on multiple datasets, verifying its effectiveness and sophistication.