Remote sensing change detection (CD) has achieved remarkable progress in recent years. However, little attention has been paid to generalizable change detection (GCD) methods that can effectively generalize to unseen scenarios or domains beyond the training distribution. The major challenges in GCD arise from domain diversity and bitemporal domain shifts in remote sensing images, caused by variations in imaging platforms, acquisition times, geographic regions, and observed events. To tackle these challenges, we propose GenCD, a GCD framework built upon vision foundation models (VFMs). Specifically, GenCD introduces two key components: (1) a Low-Rank Exchange Adaptation (LREA) strategy of VFMs that aligns bitemporal representations while preserving the generalization capacity of VFMs on single-temporal inputs; and (2) a Token-Guided Feature Refinement (TGFR) mechanism that leverages an input-independent token as a guide to refine difference features, improving the discrimination between changed and unchanged regions. We conduct extensive cross-dataset evaluations on eight diverse datasets across three binary CD tasks: land cover, land use, and building-only CD. The results consistently demonstrate the superior generalization of GenCD over SoTA methods, highlighting its effectiveness in GCD.

Automated road segmentation from remote sensing imagery remains a fundamental challenge in Earth observation systems. The primary bottleneck lies in acquiring dense pixel-wise annotations, which is both labor-intensive and time-prohibitive. This article presents DualStrip-Net, a novel deep learning framework for weakly supervised and semi-supervised road segmentation that effectively handles both sparse annotations and limited labeled data. Unlike conventional convolutional neural network (CNN)-based segmentation methods that lack explicit road topology modeling, DualStrip-Net exploits the inherent linear topology of road networks through a dual-stream architecture that combines patch-level annotation strategy and strip-based feature learning. The framework captures road characteristics through orthogonal strip processing in horizontal and vertical orientations. The proposed DualStrip Learning mechanism enables robust feature representation of road structures through complementary views. Extensive evaluations on the DeepGlobe, Massachusetts, and CHN6-CUG benchmark datasets demonstrate that DualStrip-Net achieves superior performance in both weakly supervised and semi-supervised settings. Notably, with only 20% of labeled training data, our method outperforms the supervised-only baselines on both Massachusetts and CHN6-CUG datasets. The code is available athttps://github.com/jasonnhu/DualStrip-Net/.

Graph anomaly detection (GAD) has gained increasing attention in various attribute graph applications, i.e., social communication and financial fraud transaction networks. Recently, graph contrastive learning (GCL)-based methods have been widely adopted as the mainstream for GAD with remarkable success. However, existing GCL strategies in GAD mainly focus on node-node and node-subgraph contrast and fail to explore subgraph-subgraph level comparison. Furthermore, the different sizes or component node indices of the sampled subgraph pairs may cause the "nonaligned" issue, making it difficult to accurately measure the similarity of subgraph pairs. In this article, we propose a novel subgraph-aligned multiview contrastive approach for graph anomaly detection, named SAMCL, which fills the subgraph-subgraph contrastive-level blank for GAD tasks. Specifically, we first generate the multiview augmented subgraphs by capturing different neighbors of target nodes forming contrasting subgraph pairs. Then, to fulfill the nonaligned subgraph pair contrast, we propose a subgraph-aligned strategy that estimates similarities with the Earth mover's distance (EMD) of both considering the node embedding distributions and typology awareness. With the newly established similarity measure for subgraphs, we conduct the interview subgraph-aligned contrastive learning module to better detect changes for nodes with different local subgraphs. Moreover, we conduct intraview node-subgraph contrastive learning to supplement richer information on abnormalities. Finally, we also employ the node reconstruction task for the masked subgraph to measure the local change of the target node. Finally, the anomaly score for each node is jointly calculated by these three modules. Extensive experiments conducted on benchmark datasets verify the effectiveness of our approach compared to existing state-of-the-art (SOTA) methods with significant performance gains (up to 6.36% improvement on ACM). Our code can be verified at https://github.com/hujingtao/SAMCL.

The fundamental goal of deep multi-view clustering is to achieve preferable task performance through inter-view cooperation. Although numerous DMVC approaches have been proposed, the collaboration role of individual views have not been well investigated in existing literature. Moreover, how to further enhance view cooperation for better fusion still needs to be explored. In this paper, we firstly consider DMVC as an unsupervised cooperative game where each view can be regarded as a participant. Then, we introduce the Shapley value and propose a novel MVC framework termed Shapley-based Cooperation Enhancing Multi-view Clustering (SCE-MVC), which evaluates view cooperation with game theory. Specially, we employ the optimal transport distance between fused cluster distributions and single view component as the utility function for computing shapley values. Afterwards, we apply shapley values to assess the contribution of each view and utilize these contributions to promote view cooperation. Comprehensive experimental results well support the effectiveness of our framework adopting to existing DMVC frameworks, demonstrating the importance and necessity of enhancing the cooperation among views.

Rapid dynamical pattern recognition based on the deterministic learning method (DLM-based RDPR) aims to rapidly recognize the most similar dynamical pattern pair from perspectives of differences in inherent system dynamics. The basic mechanism is to use available recognition errors to reflect the differences in the dynamics of dynamical pattern pairs and then to make a decision based on a minimal recognition error (MRE) principle. This article focuses on providing a rigorous theoretical analysis of the MRE principle in DLM-based RDPR under the sampled-data framework. Specifically, we seek a unified methodology from the similarity definition to the measure implementation and then to derive general sufficient conditions and necessary conditions for the MRE principle. The main idea is to: 1) from the average signal energy aspect, define a time-dependent dynamics-based similarity in dynamical pattern pairs and reestablish the measure of recognition errors generated from the DLM-based RDPR; 2) introduce the energy-based Lyapunov method to establish the interrelation between the dynamical distance and the recognition error; and 3) derive sufficient conditions and necessary conditions from two directions of the interrelation. The proposed conditions distinguish themselves from virtually all of the existing DLM-based RDPR works with only sufficient conditions in the sense that it is shown in a rigorous analysis that under what conditions, the pattern pair recognized based on the MRE principle is indeed the most similar one. Therefore, the proposed work makes the DLM-based RDPR possess good interpretability and provides strong theoretical guidance in engineering applications.

Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has been widely applied in many real-world applications. The primary goal of GAD is to capture anomalous nodes from graph datasets, which evidently deviate from the majority of nodes. Recent methods have paid attention to various scales of contrastive strategies for GAD, i.e., node-subgraph and node-node contrasts. However, they neglect the subgraph-subgraph comparison information which the normal and abnormal subgraph pairs behave differently in terms of embeddings and structures in GAD, resulting in sub-optimal task performance. In this paper, we fulfill the above idea in the proposed multi-view multi-scale contrastive learning framework with subgraph-subgraph contrast for the first practice. To be specific, we regard the original input graph as the first view and generate the second view by graph augmentation with edge modifications. With the guidance of maximizing the similarity of the subgraph pairs, the proposed subgraph-subgraph contrast contributes to more robust subgraph embeddings despite of the structure variation. Moreover, the introduced subgraph-subgraph contrast cooperates well with the widely-adopted node-subgraph and node-node contrastive counterparts for mutual GAD performance promotions. Besides, we also conduct sufficient experiments to investigate the impact of different graph augmentation approaches on detection performance. The comprehensive experimental results well demonstrate the superiority of our method compared with the state-of-the-art approaches and the effectiveness of the multi-view subgraph pair contrastive strategy for the GAD task. The source code is released at https://github.com/FelixDJC/GRADATE.

Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to improve the quality of node embeddings for GAD. Despite their significant advances in detection performance, there is still a relative dearth of research on the properties of the task. GAD aims to discern the anomalies that deviate from most nodes. However, the model is prone to learn the pattern of normal samples which make up the majority of samples. Meanwhile, anomalies can be easily detected when their behaviors differ from normality. Therefore, the performance can be further improved by enhancing the ability to learn the normal pattern. To this end, we propose a normality learning-based GAD framework via multi-scale contrastive learning networks (NLGAD for abbreviation). Specifically, we first initialize the model with the contrastive networks on different scales. To provide sufficient and reliable normal nodes for normality learning, we design an effective hybrid strategy for normality selection. Finally, the model is refined with the only input of reliable normal nodes and learns a more accurate estimate of normality so that anomalous nodes can be more easily distinguished. Eventually, extensive experiments on six benchmark graph datasets demonstrate the effectiveness of our normality learning-based scheme on GAD. Notably, the proposed algorithm improves the detection performance (up to 5.89% AUC gain) compared with the state-of-the-art methods. The source code is released at https://github.com/FelixDJC/NLGAD.

Road connectivity is vital in road extraction for accurate vehicle navigation. However, the segmentation-based methods fail to model the connectivity resulting in broken road segments. Therefore, we propose a Location-Guided Network (LGNet) for promoting connectivity performance in a very effective and efficient way. Specifically, an auxiliary Road Location Prediction (RLP) task is designed to obtain global road connectivity information, which improves the performance of road segmentation. The RLP can predict the location coordinates of the whole roads with row anchors and column anchors. By aggregating the global location context to the segmentation branch with a location-guided decoder (LG-Decoder), the features can finally capture the connectivity of each road segment. Overall, LGNet has the following advantages: 1) The proposed RLP and LCG can plug into any encoder-decoder network and achieve an impressive performance. 2) High computational efficiency. In comparison with the multi-branch method, our proposed LGNet requires about 6× fewer GFLOPs. 3) The superior road connectivity performance. A series of experiments are conducted on two road extraction data sets (SpaceNet and DeepGlobe), confirming the effectiveness of the LGNet.

Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training and testing models using unbiased datasets, which does not align well with real-world applications. Therefore, we propose a new benchmark dataset to study the transferability of height estimation models in a cross-dataset setting. To this end, we first design and construct a large-scale benchmark dataset for cross-dataset transfer learning on the height estimation task. This benchmark dataset includes a newly proposed large-scale synthetic dataset, a newly collected real-world dataset, and four existing datasets from different cities. Next, a new experimental protocol,few-shot cross-dataset transfer, is designed. Furthermore, in this paper, we propose a scale-deformable convolution module to enhance the window-based Transformer for handling the scale-variation problem in the height estimation task. Experimental results have demonstrated the effectiveness of the proposed methods in traditional and cross-dataset transfer settings. The datasets and codes are publicly available at https://mediatum.ub.tum.de/1662763 and https://thebenchmarkh.github.io/.

This article investigates dynamical pattern recognition for a class of univariate time-series data. These data are sampled from the output of dynamical systems with uncertain dynamics. Based on deterministic learning, a rapid recognition approach is presented from the viewpoint of the sample-data observer. It comprises two phases: 1) training and 2) recognition. In the training phase, locally accurate dynamical modeling of the underlying dynamics of training time series can be accomplished by merging a sampled-data observer and radial basis function network (RBFN) identifiers. In the recognition phase, several RBFN-based estimators with non-high-gain designs are constructed. In this case, the stability analysis of the generated estimator error systems will conduce to conduct non-high-gain recognition of a test time series. We demonstrate that these estimator errors can depict dynamics differences between the dynamical patterns of the test and training time-series data. Based on the average$L_{1}$norms of the output errors, a decision-making scheme is developed to generate recognition results rapidly. More concise and relaxed recognition conditions are derived through rigorous analysis to ensure accurate recognition results. Simulation studies exemplify the effectiveness of the presented approach.

Unsupervised video anomaly detection (UVAD) intends to discern anomalous events from fully unlabeled videos. However, existing UVAD methods suffer from poor performance. Inspired by recent masked autoencoder (MAE) [1], we propose Temporal Masked Auto-Encoding (TMAE) as an effective end-to-end UVAD method. Specifically, we first denote video events by spatial-temporal cubes (STCs), which are built by temporally consecutive foreground patches from unlabeled videos. Then, half of patches in an STC are masked along the temporal dimension, while a vision transformer (ViT) is trained to exploit unmasked patches to predict masked patches. The rare and unusual nature of anomaly will result in a poorer prediction for anomalous events, which enables us to discriminate anomalies from unlabeled videos and compute the anomaly scores. Furthermore, to utilize motion clues in videos, we also propose to apply TMAE on optical flow, which can further boost performance. Experiments show that TMAE significantly outperforms existing UVAD methods by a notable margin (3.9%–6.6% AUC).

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure. Moreover, flagging OoD cases may support human readers in identifying incidental findings. Due to the increased interest in OoD algorithms, benchmarks for different domains have recently been established. In the medical imaging domain, for which reliable predictions are often essential, an open benchmark has been missing. We introduce the Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain. The analysis of the submitted algorithms shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice. We also see a strong correlation between challenge ranking and performance on a simple toy test set, indicating that this might be a valuable addition as a proxy dataset during anomaly detection algorithm development.

A unified approach is proposed to design sampled-data observers for a certain type of unknown nonlinear systems undergoing recurrent motions based on deterministic learning in this article. First, a discrete-time implementation of high-gain observer (HGO) is utilized to obtain state trajectory from sampled output measurements. By taking the recurrent estimated trajectory as inputs to a dynamical radial basis function network (RBFN), a partial persistent exciting (PE) condition is satisfied, and a locally accurate approximation of nonlinear dynamics can be realized along the estimated sampled-data trajectory. Second, an RBFN-based observer consisting of the obtained dynamics from the process of deterministic learning is designed. Without resorting to high gains, the RBFN-based observer is shown capable of achieving correct state observation. The novelty of this article lies in that, by incorporating deterministic learning with the discrete-time HGO, the nonlinear dynamics can be accurately approximated along the estimated trajectory, and such obtained knowledge can then be utilized to realize nonhigh-gain state estimation for the same or similar sampled-data systems. Simulation is performed to validate the effectiveness of the proposed approach.

Road extraction from satellite images is usually corrupted with several disconnected segments so that it does not satisfy the real application. The segmentation-based methods fail to correct separated roads due to the incompleteness information. Therefore, this paper introduces auxiliary Road Location Prediction(RLP), a task leveraging global context information to help road segmentation infer each road segment. The auxiliary task has two branches: horizontal location prediction and vertical location prediction which can predict locations of all the roads. By combining road segmentation and RLP, road extraction performance is effectively improved. As a result, the additional training signals help the primary road segmentation task to aggregate surrounding scene information to reason about its connectivity. The experiments on two public datasets have demonstrated the effectiveness of the proposed method.

Multi-view clustering (MVC) optimally integrates complementary information from different views to improve clustering performance. Although demonstrating promising performance in many applications, we observe that most of existing methods directly combine multiple views to learn an optimal similarity for clustering. These methods would cause intensive computational complexity and over-complicated optimization. In this paper, we theoretically uncover the connection between existing k-means clustering and the alignment between base partitions and consensus partition. Based on this observation, we propose a simple but effective multi-view algorithm termed {Multi-view Clustering via Late Fusion Alignment Maximization (MVC-LFA)}. In specific, MVC-LFA proposes to maximally align the consensus partition with the weighted base partitions. Such a criterion is beneficial to significantly reduce the computational complexity and simplify the optimization procedure. Furthermore, we design a three-step iterative algorithm to solve the new resultant optimization problem with theoretically guaranteed convergence. Extensive experiments on five multi-view benchmark datasets demonstrate the effectiveness and efficiency of the proposed MVC-LFA.

Video anomaly detection is a valuable but challenging task, especially in the field of surveillance videos for public safety. Almost all existing methods tackle the problem under the supervised setting and only a few attempts are conducted on the unsupervised learning. To avoid the cost of labeling training videos, this paper proposes to discriminate anomaly by a novel two-stage framework in a fully unsupervised manner. Unlike previous unsupervised approaches using local change detection to discover abnormality, our method enjoys the global information from video context by considering the pair-wise similarity of all video events. In this way, our method formulates video anomaly detection as an extension of unsupervised one-class learning, which has not been explored in the literature of video anomaly detection. Specifically, our method consists of two stages: The first stage of our kernel-based method, named Low-rank based Unsupervised One-class Learning with Ridge Regression (LR-UOCL-RR), reformulates the optimization goal of UOCL with ridge regression to avoid expensive computation, which enables our method to handle massive unlabeled data from videos. In the second stage, the estimated normal video events from the first stage are fed into the one-class support vector machine to refine the profile around normal events and enhance the performance. The experimental results conducted on two challenging video benchmarks indicate that our method is considerably superior, up to 15:7% AUC gain, to the state-of-the-art methods in the unsupervised anomaly detection task and even better than several supervised approaches.