Although numerous semi-supervised learning methods have been elaborately designed for hyperspectral image (HSI) classification, most existing semi-supervised learning paradigms still rely on a closed-set assumption. These methods implicitly assume that the category spaces of labeled and unlabeled samples are completely aligned, that is, all unlabeled samples must belong to a pre-defined known category set. However, the closed-set assumption is particularly problematic in practical remote sensing scenarios because partial unlabeled data inevitably belong to unknown categories. To address this challenge, this paper proposes a reconstruction-contrast coupling learning (ReCo2L) method for open-set semi-supervised HSI classification, fully leveraging the complementarity between masked feature reconstruction learning and contrastive learning to enhance the encoder’s local detail sensitivity and global discriminative ability. Specifically, we first apply a masked feature reconstruction learning with an adaptive masking strategy to enhance the encoder’s ability to capture local details by high-quality spectral-spatial feature reconstruction. Then, we employ contrastive learning to strengthen the encoder’s capability to extract global characteristics by pulling semantically similar samples closer and pushing dissimilar ones farther apart in the feature space. Finally, a pixel-prototype deviation loss is proposed to further improve both inter-category distinguishability and intra-category compactness by reducing the distances between labeled sample features and their corresponding class anchors. Extensive experiments on three benchmark datasets demonstrate that our proposed ReCo2L achieves superior classification performance in both known and unknown categories and significantly surpasses 10 state-of-the-art HSI classification methods. The code will be available at https://github.com/repository-AI-chen/ReCo2L.

Domain adaptation in medical image segmentation enables pre-trained models to generalize to new target domains. Given limited annotated data and privacy constraints, Source-Free Active Domain Adaptation (SFADA) methods provide promising solutions by selecting a few target samples for labeling without accessing source samples. However, in a fully source-free setting, existing works have not fully explored how to select these target samples in a class-balanced manner and how to conduct robust model adaptation using both labeled and unlabeled samples. In this study, we discover that boundary samples with source-like semantics but sharp predictive discrepancies are beneficial for SFADA. We define these samples as the most influential points and propose a slice-wise framework using influential points learning to explore them. Specifically, we detect source-like samples to retain source-specific knowledge. For each target sample, an adaptive K-nearest neighbor algorithm based on local density is introduced to construct neighborhoods of source-like samples for knowledge transfer. We then propose a class-balanced Kullback-Leibler divergence for these neighborhoods, calculating it to obtain an influential score ranking. A diverse subset of the highest-ranked target samples (considered influential points) is manually annotated. Furthermore, we design a progressive teacher model to facilitate SFADA for medical image segmentation. With the guidance of influential points, this model independently generates and utilizes pseudo-labels to mitigate error accumulation. To further suppress noise, curriculum learning is incorporated into the model to progressively leverage reliable supervision signals from pseudo-labels. Experiments on multiple benchmarks demonstrate that our method outperforms state-of-the-art methods even with only 2.5% of the labeling budget.

Semi-supervised hyperspectral image (HSI) classification methods focus on exploring the spectral and spatial information of unlabeled samples. However, existing methods generally follow the closed-set setting, assuming that unlabeled samples do not contain novel classes, which is hard to hold in practical applications. This paper aims to study semi-supervised HSI classification in the open-set setting, i.e., unlabeled samples fall into novel classes, and proposes a class-aware consistency learning (CACL) method. First, to explore discriminative spectral-spatial features, a position-aware transformer is developed, which effectively models spatial position priors between the center pixel and its neighboring pixels via a symmetric position-aware encoding. Then, to reduce the interference from novel class samples on the model’s discrimination, a prototype-driven consistency learning is proposed, which accurately selects unlabeled samples belonging to known classes via a known class sampler, and efficiently utilizes their spectral-spatial information by modeling consistent predictions across different views. Finally, to further improve the distinguishability between known classes, a prototype contrastive optimization is proposed to decrease the distance between samples from the same class and increase the distances between those from different classes in the feature domain. Furthermore, an adaptive segmentation threshold is designed to accurately predict known classes and reject novel classes. Extensive experiments verify that our CACL outperforms the state-of-the-art methods, achieving the overall accuracy of 81.65%, 88.61%, and 92.88% on the Indian Pines, Salinas, and Pavia University datasets, with 10 labeled samples in each known class. The code is available at https://github.com/rock-in/CACL-main.

GUI models encapsulate the desired visual appearance and interactive behaviors of applications, facilitating various downstream tasks like model-based testing (MBT). Manually constructing high-quality GUI models is not only labor-intensive and costly but also prone to errors, particularly as applications evolve and require frequent model updates. Existing automated approaches for GUI model generation heavily rely on reverse engineering, where the models are abstractions of the code. As a result, they are not suitable for MBT to test functional issues because they are consistent with the code. Meanwhile, valuable development artifacts such as UI/UX design documents, which reflect design intentions, are often overlooked. In this paper, a novel approach named DemGen is proposed to seek a unique pathway for GUI model generation. Leveraging design documents, DemGen employs computer vision pre-trained models in conjunction with a rule-based correction mechanism to identify GUI elements and their intended behaviors as defined in those documents. Subsequently, the identified content is transformed into a formal GUI model adhering to the IFML modeling language. Our evaluation, conducted in collaboration with an industry partner on commercial applications, demonstrates the effectiveness and efficiency of DemGen in GUI element recognition and GUI model generation. Moreover, we conducted a comparative analysis of manual, automated, and hybrid modeling techniques, assessing the usefulness of generated models on MBT tasks.

Pseudo-label learning-based methods usually regard class confidence above a certain threshold for unlabeled samples as pseudo-labels, which may result in pseudo-labels still containing wrong labels. In this letter, we propose a prototype-based pseudo-label refinement (PPLR) for semi-supervised hyperspectral image classification. The proposed PPLR filters wrong labels from pseudo-labels using class prototypes, which can improve the discrimination of the network. First, PPLR uses multi-head attentions to extract the spectral-spatial features, and designs an adaptive threshold that can be dynamically adjusted to generate high-confidence pseudo-labels. Then, PPLR constructs class prototypes for different categories using labeled sample features and unlabeled sample features with refined pseudo-labels to improve the quality of pseudo-labels by filtering wrong labels. Finally, PPLR further assigns reliable weights to these pseudo-labels in calculating their supervised loss, and introduces a center loss to improve the discrimination of features. When 10 labeled samples per category are utilized for training, PPLR achieves the overall accuracies of 82.11%, 86.70% and 92.50% on the Indian Pines, Houston2013 and Salinas datasets, respectively.

Recently, pseudo-label-based deep learning methods have shown excellent performance in semi-supervised hyperspectral image (HSI) classification. These methods usually select high-confidence unlabeled samples to help optimize backbone classification networks. However, a large number of remaining low-confidence unlabeled samples, which contain rich land-covers information, are underutilized. In this paper, we propose a pseudo-label-based unreliable sample learning (PUSL) method to fully exploit low-confidence unlabeled samples for semi-supervised HSI classification. Firstly, to avoid overfitting the spatial distribution of labeled samples, we build a position-free transformer (PFT) as the backbone classification network. Secondly, PFT is initially trained with labeled samples in a supervised learning manner to obtain an initial classifier, which is then used to split unlabeled samples into reliable and unreliable unlabeled samples based on the predicted confidence. Thirdly, reliable unlabeled samples participate in training along with labeled samples. Finally, unreliable unlabeled samples are treated as negative samples for corresponding categories to improve the discrimination of PFT in a contrastive learning paradigm. Extensive experiments on three HSI datasets demonstrate that PUSL outperforms compared methods.

Accurately obtaining information of road traffic conditions is of great significance to people’s travel planning and arrangement of social shared resources, and has become a major research focus in the field of smart cities. Accurately predicting road conditions poses a huge challenge due to the complex spatial correlations and nonlinear temporal dependencies of real-time traffic networks. In this paper we propose a Multi-Range Attention-Based Dynamic Graph Convolutional Network (MRA-DGCN) to model complex traffic networks. The MRA-DGCN model uses a bicomponent modules to separate different periodicity to extract refined traffic signal. In the MRA-DGCN model, we use the adaptive spatial-temporal network block (ASTnet block), which includes dynamic graph convolution and temporal attention, to mine complex spatial correlations and nonlinear temporal dependencies, respectively. In the adaptive spatial-temporal network block, we use dynamically generated adjacency matrices instead of existing distance-based adjacency matrices to perform graph convolution operations to aggregate information between nodes during model training. Instead of hierarchically extracting spatial-temporal signal, we adopt temporal attention to capture the spatial-temporal information synchronously to improve the prediction performance. Furthermore, we propose a residual gated network to control the flow of information passed to the next hidden layer to enhance the predictive accuracy. Extensive experiments on two real-world traffic datasets, METR-LA and PeMS-BAY, show that the MRA-DGCN achieves the state-of-the-art results.

Accurate long-term traffic forecasting is of great significance to intelligent transportation systems, but long-term traffic forecasting is very challenging due to the complexity of the spatial and temporal relationship of traffic data. This paper proposes a model for prediction of traffic data. The model adopts an encoder-decoder architecture, in which the encoder and decoder are composed of a Graph Generator module, a Multi-Head Convolutional Self-Attention module, and a Dynamic Convolution Recurrent module to simulate the impact of spatial and temporal factors on traffic conditions. The encoder encodes the input features and the decoder predicts the output sequence. Between the encoder and the decoder, we use a Transformation mechanism to re-encode the traffic features to generate new sequence representations as the input of the decoder, which helps alleviate the problem of error propagation during prediction. We conduct the traffic prediction task on two real-world traffic datasets and the experimental results show that our model performs better than other baseline models.

We consider the learning and prediction of nonlinear time series generated by a latent symplectic map. A special case is (not necessarily separable) Hamiltonian systems, whose solution flows give such symplectic maps. For this special case, both generic approaches based on learning the vector field of the latent ODE and specialized approaches based on learning the Hamiltonian that generates the vector field exist. Our method, however, is different as it does not rely on the vector field nor assume its existence; instead, it directly learns the symplectic evolution map in discrete time. Moreover, we do so by representing the symplectic map via a generating function, which we approximate by a neural network (hence the name GFNN). This way, our approximation of the evolution map is always \emph{exactly} symplectic. This additional geometric structure allows the local prediction error at each step to accumulate in a controlled fashion, and we will prove, under reasonable assumptions, that the global prediction error grows at most \emph{linearly} with long prediction time, which significantly improves an otherwise exponential growth. In addition, as a map-based and thus purely data-driven method, GFNN avoids two additional sources of inaccuracies common in vector-field based approaches, namely the error in approximating the vector field by finite difference of the data, and the error in numerical integration of the vector field for making predictions. Numerical experiments further demonstrate our claims.