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.

Missing attribute values frequently affect data classification, reducing accuracy as most models rely on complete datasets. Imputing missing values is typically used to restore data completeness, which is essential for building models. The effectiveness of imputation significantly impacts the classification accuracy. Therefore, improving imputed values’ quality is crucial for better classification outcomes. Here, we introduce a new distribution alignment framework (DAF) to address classification issues with complete training data but incomplete test data. Initially, DAF imputes missing test data values using mean vectors from complete training data, minimizing the first-order distributional discrepancies. Next, it aligns the second-order statistical distributions, specifically covariance matrices, of both training and imputed test data to derive a feature transformation matrix. This matrix generates new feature representations for the incomplete test data. The classifier trained on the complete training data then classifies the imputed test data under this new feature representation. The experiments on several benchmark datasets show that DAF usually outperforms many advanced methods, achieving the higher classification performance.

In pattern recognition, where the labeled data is scarce, transfer learning (also called domain adaptation in some cases) methods frequently come into play to transfer knowledge from the source domains to bolster the construction of classification models within the target domain. The judicious fusion of information from multiple source domains typically enhances classification precision. In light of this, we introduce a new Multi-source Distributed Transfer Learning (MDTL) framework designed to adeptly integrate complementary information across various source domains through the application of belief functions. In this approach, the distributions of each source and target domain are aligned independently. Subsequently, the resultant soft classification outcomes, facilitated by different source domains, are amalgamated using belief functions. This integration incorporates novel weighting factors that consider both distribution discrepancies and classifier effectiveness. The effectiveness of MDTL was assessed against a range of related methods, and the experimental findings confirm that it markedly improves classification accuracy in the target domain.

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.

For incomplete data classification, missing attribute values are often estimated by imputation methods before building classifiers. The estimated attribute values are not actual attribute values. Thus, the distributions of data will be changed after imputing, and this phenomenon often results in degradation of classification performance. Here, we propose a new framework called integration of multikinds imputation with covariance adaptation (MICA) based on evidence theory (ET) to effectively deal with the classification problem with incomplete training data and complete test data. In MICA, we first employ different kinds of imputation methods to obtain multiple imputed training datasets. In general, the distributions of each imputed training dataset and test dataset will be different. A covariance adaptation module (CAM) is then developed to reduce the distribution difference of each imputed training dataset and test dataset. Then, multiple classifiers can be learned on the multiple imputed training datasets, and they are complementary to each other. For a test pattern, we can combine the multiple pieces of soft classification results yielded by these classifiers based on ET to obtain better classification performance. However, the reliabilities/weights of different imputed training datasets are usually different, so the soft classification results cannot be treated equally during fusion. We propose to use covariance difference across datasets and accuracy of imputed training data to estimate the weights. Finally, the soft classification results discounted by the estimated weights are combined by ET to make the final class decision. MICA was compared with a variety of related methods on several datasets, and the experimental results demonstrate that this new method can significantly improve the classification performance.

In the context of distribution inconsistent data classification, addressing distribution shift is crucial, typically accomplished through domain adaptation (DA) techniques. Once distributions are aligned between the source and target domains, the problem transforms into a conventional recognition task. This article introduces a new method called multitarget DA based on Evidence Theory (MET). For a given target domain, a random merger with other target domains is performed, generating distinct new target domains. Domain-invariant features corresponding to each new target domain are learned by minimizing distribution discrepancies separately between the source and different new target domains. The merging of target domains alters the distribution of the new target domain, leading to variations in the retained information within the learned domain-invariant features. For a query pattern in this target domain, multiple soft classification results (CCR) are obtained after aligning the distributions of the source and different new target domains. These soft CCR complement each other, and evidence theory is employed as a tool to represent and combine uncertain information, fusing these results. The weights for this fusion are automatically learned by minimizing the mean squared error between the combined results and the ground truth on labeled source domain data. The final class decision is determined through the weighted evidential combination of multiple pieces of soft CCR. MET is assessed on several datasets (i.e., Office+Caltech-10, VLSC, and V-RSIR) and compared to various advanced DA methods (e.g., GNN, MT, PAL, PTD, and so on) to validate its effectiveness. The experimental results demonstrate that MET usually can obtain a higher classification performance (i.e., the accuracy can be improved by 2% compared to many methods in most cases).

Synthetic aperture radar (SAR) target recognition based on deep convolutional neural networks (CNN) has achieved great success. Whereas, CNN usually needs massive labeled data to learn. In some cases, it may be difficult to collect a large number of labeled data, especially in SAR target recognition field. Thus, we develop a new method called SAR target recognition based on convolutional feature aggregation and decision combination (FADC) to improve the classification accuracy when labeled SAR data is limited. In FADC, we propose to concatenate the feature maps of different convolutional layers to extract discriminative feature. Then, the first-order statistical features of different layers are used to train extra classifiers. We can obtain two pieces of soft classification results yielded by softmax layer and extra classifiers for a query SAR target image. These soft classification results are combined by weighted arithmetic average rule whose weights are learnt by minimizing the mean squared error between fusion results and ground truth on labeled SAR target images. FADC was tested on MSTAR dataset, and the experiment results demonstrate that it can effectively improve the classification accuracy compared with a variety of advanced methods.

Low-light remote sensing (RS) 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 RS images. convolutional neural networks (CNNs), 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 RS images. From another perspective, the 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 RS image enhancement. Specifically, this challenging task of low-light enhancement is divided into two more manageable subtasks: 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. In addition, we have constructed two dark light RS datasets to address the current lack of datasets in dark light RS image enhancement. Extensive evaluations show that our method outperforms existing state-of-the-art methods. The code is available athttps://github.com/iijjlk/DFFN.

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection.

Partial label learning (PLL), an important branch of weakly supervised learning, addresses the problem that each instance is associated with a set of candidate labels and only one is correct. In this paper, a novel adversarial model PL-MIGAN is proposed to simultaneously mitigate two fundamental issues of generative adversarial networks (GANs) in PLL: label disambiguation performance of discriminator and instance synthesis quality of generator. First of all, multi-class support vector machines (SVMs) applied in discriminator to disambiguate the candidate labels and identify fake instances. This strategy not only improves the disadvantage that traditional supervised loss is unable to perform disambiguation but also reduces the influence of cumulative error caused by noise label propagation. Furthermore, a partial contrastive loss is constructed to extend the self-supervised contrastive approach to PLL, allowing us to effectively leverage ambiguous labels information. Finally, the generator jointly employ mutual information (MI) and partial contrastive loss to estimate the latent distribution of each class label. In addition, in order to reduce the impact of ambiguous information, an iteratively optimization procedure is designed to update the label confidence matrix as conditional information guides the generation of instance classes. As adversarial learning proceeds, both the discriminator and the generator alternately and iteratively boost their performance. Simulation results reveal the overwhelming performance of PL-MIGAN.

Partial Label Learning (PLL) is a weakly supervised learning framework where each training instance is associated with more than one candidate label. This learning method is dedicated to finding out the true label for each training instance. Most of the current PLL algorithms directly disambiguate the candidate labels without correcting the disambiguated results, making the algorithms vulnerable to the influence of instances easily misjudged. In this paper, GraphDCN, an innovative disambiguation correction net with inductive graph representation learning model is proposed. GraphDCN consists of a disambiguation model and a correction model. For a given instance, the disambiguation model tries to fit its underlying ground-truth label through the candidate label distribution of the instances connected with the given one, while the correction model tries to maximize the distance between the disambiguated labels and non-candidate labels, and uses the label probability thresholds to correct the disambiguated labels that may be wrong. As the training goes on, both the disambiguation and correction models alternately and iteratively boost their performance. Moreover, when considering the implementation of the disambiguation model, a partial cross entropy formulation is proposed to estimate the ground-truth label loss by updating the ambiguity confidence matrix, which can be proved satisfying convergence in PLL. Simulation results reveal the overwhelming performance of GraphDCN.