Most of the recently proposed deep learning-based speech enhancement techniques have focused on designing the neural network architectures as a black box. However, it is often beneficial to understand what kinds of hidden representations the model ha...
As dataset environments evolve, the adaptability of deep models has weakened due to biases in training data collection. Consequently, a critical challenge has emerged: enabling models to effectively learn invariant features across diverse environment...
Causality has become a powerful tool for addressing the out-of-distribution (OOD) generalization problem, with the idea of invariant causal features across domains of interest. Most existing methods for learning invariant features are based on optimi...
To promote the development of deep learning for feature matching, image registration, and three-dimensional reconstruction, we propose a method of constructing a deep learning benchmark dataset for affine-invariant feature matching. Existing images o...
In spacecraft rendezvous and docking, traditional methods that rely on inertial navigation and sensor data face challenges due to sensor inaccuracies, noise, and a lack of multi-approach assurance. Focusing on exploring a new approach as assistance,...
Multimodal remote sensing data provide important data support for large-scale, all-weather monitoring of offshore oil and gas platforms. However, differences in imaging modalities and acquisition conditions introduce significant domain shift, which l...
Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual–real registration for Augmented Reality (AR) in outdoor environments. In this...
Kinship verification aims to determine whether two given persons are blood relatives. This technique can be leveraged in many real-world scenarios, such as finding missing people, identification of kinship in forensic medicine, and certain types of i...
Human activity recognition has become an important component of human–computer interaction and ubiquitous computing. Among various sensing technologies, WiFi-based gesture recognition has attracted increasing attention due to its contactless na...
Data-driven text classification technology is crucial for understanding and managing a large number of entrepreneurial policy-related texts, yet it is hindered by two primary challenges. First, the intricate, multi-faceted nature of policy documents...
Cross-condition fault diagnosis of cotton harvester picking-head drivetrains remains challenging due to significant distribution discrepancies in vibration signals under different operating conditions. Existing transfer learning approaches predominan...
The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation an...
Unsupervised domain adaptation (UDA) based on adversarial learning for remote-sensing scene classification has become a research hotspot because of the need to alleviating the lack of annotated training data. Existing methods train classifiers accord...
In recent years, the method of deep learning has been widely used in the field of fault diagnosis of mechanical equipment due to its strong feature extraction and other advantages such as high efficiency, portability, and so on. However, at present,...
Unmanned aerial vehicles (UAVs) have become indispensable tools for surveillance, enabled by their ability to capture multi-perspective imagery in dynamic environments. Among critical UAV-based tasks, cross-platform person search—detecting and...
With the proliferation of mobile devices and payment systems in modern financial services, there is an increasing need to process and analyze continuous streams of transaction data for credit risk assessment. Leveraging the inherent symmetries in fin...
Skeleton-based human action recognition is an important problem in applied vision systems, yet many existing approaches depend on a single skeleton descriptor or a single feature-learning mechanism. This restriction can weaken the representation of l...
Object detection in remote sensing imagery plays a pivotal role in various applications, including aerial surveillance and urban planning. Despite its significance, the task remains challenging due to cluttered backgrounds, the arbitrary orientations...
Parkinson’s disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific obje...
The COVID-19 pandemic has resulted in a global health crisis. The rapid spread of the virus has led to the infection of a significant population and millions of deaths worldwide. Therefore, the world is in urgent need of a fast and accurate COVID-19...
Researchers encounter substantial challenges in medical image classification, mainly due to limited image resolution and low signal-to-noise ratios. This situation makes it difficult for deep learning algorithms to identify abnormal regions based sol...
With the introduction of the Convolutional Neural Network (CNN) and other classical algorithms, facial and object recognition have made significant progress. However, in a situation where there are few label examples or the environment is not ideal,...
Accurate short-term load forecasting (STLF) is essential for power grid systems to ensure reliability, security and cost efficiency. Thanks to advanced smart sensor technologies, time-series data related to power load can be captured for STLF. Recent...
The increasing number of daily notifications generated by smartphones and wearable devices increases mental burdens, deteriorates productivity, and results in energy waste. These phenomena are exacerbated by emerging use cases in which users are wear...
Electronic nose (E-nose), a kind of instrument which combines with the gas sensor and the corresponding pattern recognition algorithm, is used to detect the type and concentration of gases. However, the sensor drift will occur in realistic applicatio...
Rotation-invariant (RI) point cloud models aim to reduce sensitivity to viewpoint changes, but their performance still drops noticeably in real-world settings when local geometry is degraded by noise, occlusion, and uneven sampling. Once these distur...
Strong geometric and radiometric distortions often exist in optical wide-baseline stereo images, and some local regions can include surface discontinuities and occlusions. Digital photogrammetry and computer vision researchers have focused on automat...
The advertising market’s use of smartphones and kiosks for non-face-to-face ordering is growing. An advertising video recommender system is needed that continuously shows advertising videos that match a user’s taste and displays other advertising vid...
In the Czochralski process of silicon single-crystal growth, the V/G value at the solid–liquid interface is a key parameter affecting intrinsic crystal defects. However, online V/G detection remains difficult because the temperature gradient G...
Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware S...
Cross-domain named entity recognition (NER) is a crucial task in various practical applications, particularly when faced with the challenge of limited data availability in target domains. Existing methodologies primarily depend on feature representat...
We assume that a sufficiently large database is available, where a physical property of interest and a number of associated ruling primitive variables or observables are stored. We introduce and test two machine learning approaches to discover possib...
Contrastive learning allows us to learn general features for downstream tasks without the need for labeled data by leveraging intrinsic signals within remote sensing images. Existing contrastive learning methods encourage invariant feature learning b...
Learning invariant (causal) features for out-of-distribution (OOD) generalization have attracted extensive attention recently, and among the proposals, invariant risk minimization (IRM) is a notable solution. In spite of its theoretical promise for l...
The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction o...
Cross-domain scene classification aims to mitigate the distribution discrepancy between domains through domain adaptation techniques. With the rapid advancement of Vision–Language Models (VLMs), utilizing them for cross-domain scene classificat...
The diffused practice of pre-training Convolutional Neural Networks (CNNs) on large natural image datasets such as ImageNet causes the automatic learning of invariance to object scale variations. This, however, can be detrimental in medical imaging,...
Multimedia recommendation systems aim to accurately predict user preferences from multimodal data. However, existing methods may learn a recommendation model from spurious features, i.e., appearing to be related to an outcome but actually having no c...
Simultaneous localization and mapping (SLAM) systems play an important role in the field of automated robotics and artificial intelligence. Feature detection and matching are crucial aspects affecting the overall accuracy of the SLAM system. However,...
Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age...
Unsupervised person re-identification (Re-ID) has been proven very effective and it boosts the performance in learning representations from unlabeled data in the dataset. Most current methods have good accuracy, but there are two main problems. First...
In Hyperspectral Image (HSI) classification, acquiring large quantities of high-quality labeled samples is typically costly and impractical. Traditional deep learning methods are limited in such scenarios due to their dependence on sample quantities....
Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often su...
Feature point detection is the basis of computer vision, and the detection methods with geometric invariance and illumination invariance are the key and difficult problem in the field of feature detection. This paper proposes an illumination-invarian...
In recent years, the defect image classification method based on deep transfer learning has been widely explored and researched, and the task of source and target domains with the same painting defect image class has been solved successfully. However...
Cross-domain person re-identification is a technique for identifying the same individual across different cameras or environments that necessitates the overcoming of challenges posed by scene variations, which is a primary challenge in person re-iden...
In recent years, domain generalization-based fault diagnosis (DGFD) methods have shown significant potential in rotating machinery fault diagnosis in unseen target domains. However, these methods focus on learning domain-invariant representations via...
As mobile robots perform long-term operations in large-scale environments, coping with perceptual changes becomes an important issue recently. This paper introduces a stochastic variational inference and learning architecture that can extract conditi...
Point cloud registration is a critical problem in 3D vision tasks, and numerous learning-based point cloud registration methods have been proposed in recent years. However, a common issue with most of these methods is that their feature descriptors a...
Compared to fault diagnosis across operating conditions, the differences in data distribution between devices are more pronounced and better aligned with practical application needs. However, current research on transfer learning inadequately address...
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