SAR-optical images from different sensors can provide consistent information for scene classification. However, the utilization of unlabeled SAR-optical images in deep learning-based remote sensing image interpretation remains an open issue. In recen...
Preserving long-tail, minority information during model compression has been linked to algorithmic fairness considerations. However, this assumes that large models capture long-tail information and smaller ones do not, which raises two questions. One...
Land-use and land-cover (LULC) change detection (CD) is a pivotal research area in remote sensing applications, posing a significant challenge due to variations in illumination, radiation, and image noise between bi-temporal images. Currently, deep l...
The detection of building changes (hereafter ‘building change detection’, BCD) is a critical issue in remote sensing analysis. Accurate BCD faces challenges, such as complex scenes, radiometric differences between bi-temporal images, and...
Research in visual intelligence has shifted from task-specific convolutional networks trained on curated labels toward large-scale pretrained architectures that transfer across image, video, and language tasks with minimal fine-tuning. Existing surve...
Visual encoding models are important computational models for understanding how information is processed along the visual stream. Many improved visual encoding models have been developed from the perspective of the model architecture and the learning...
To address the limitation regarding the supervised dataset scale in the semantic recognition of newly distributed types such as wisdom teeth and missing teeth, the multi-encoding contrastive learning for dual-stream self-supervised 3D dental segmenta...
Motif occupancy identification is a binary classification task predicting the binding of DNA motif instances to transcription factors, for which several sequence-based methods have been proposed. However, through direct training, these end-to-end met...
The traditional spectrum sensing algorithm based on deep learning requires a large number of labeled samples for model training, but it is difficult to obtain them in the actual sensing scene. This paper applies self-supervised contrast learning in o...
Self-supervised learning has been successful in computer vision, and its application to medical imaging has shown great promise. This study proposes a novel self-supervised learning method for medical image classification, specifically targeting ultr...
Unsupervised anomalous sound detection, especially self-supervised methods, plays a crucial role in differentiating unknown abnormal sounds of machines from normal sounds. Self-supervised learning can be divided into two main categories: Generative a...
In recent years, contrastive learning has been a highly favored method for self-supervised representation learning, which significantly improves the unsupervised training of deep image models. Self-supervised learning is a subset of unsupervised lear...
Supervised object detection models rely on large training datasets to achieve good performance. However, data labeling is often an expensive and time-consuming task. To mitigate this challenge, self-supervised methods have been utilized to learn repr...
Self-supervised learning provides an effective way to reduce the dependence on manual annotations in remote sensing scene classification. However, existing self-supervised methods often emphasize either global semantic invariance or local structural...
We present a new method of self-supervised learning and knowledge distillation based on multi-views and multi-representations (MV–MR). MV–MR is based on the maximization of dependence between learnable embeddings from augmented and non-au...
Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervise...
This paper introduces a novel approach to leveraging features learned from both supervised and self-supervised paradigms, to improve image classification tasks, specifically for vehicle classification. Two state-of-the-art self-supervised learning me...
Early and accurate detection of plant diseases is essential for ensuring food security and maintaining sustainable agricultural productivity. However, most deep learning models for plant disease classification rely heavily on large-scale annotated da...
The existing research on deep learning for radar signal intra–pulse modulation classification is mainly based on supervised leaning techniques, which performance mainly relies on a large number of labeled samples. To overcome this limitation, a...
Clouds have an enormous influence on the hydrological cycle, Earth’s radiation budget, and climate changes. Accurate automatic recognition of cloud shape based on ground-based cloud images is beneficial to analyze the atmospheric motion state a...
The application of self-supervised learning (SSL) is increasingly imperative for advancing wireless communication technologies, particularly in scenarios with limited labeled data. Traditional data-augmentation-based SSL methods have struggled to acc...
Due to the vast geometric and radiometric differences between SAR and optical images, SAR-optical image matching remains an intractable challenge. Despite the fact that the deep learning-based matching model has achieved great success, SAR feature em...
Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to signal noise, inter-patient variability, and limited annotated data, which constrain the generalization of supervised learning approaches. This study presents a se...
Specific Emitter Identification (SEI) is a specialized technique for identifying different emitters by analyzing the unique characteristics embedded in received signals, known as Radio Frequency Fingerprints (RFFs), and SEI plays a crucial role in ci...
Rheumatic heart disease (RHD) poses a significant global health challenge, necessitating improved diagnostic tools. This study investigated the use of self-supervised multi-task learning for automated echocardiographic analysis, aiming to predict ech...
Following recent advancements in medical laboratory technology, the analysis of high-resolution renal pathological images has become increasingly important in the diagnosis and prognosis prediction of chronic nephritis. In particular, deep learning h...
Accurate pest recognition is an important foundation for intelligent plant protection, precision pesticide application, and sustainable agricultural management. However, in real field environments, pest targets are often small in scale, severely occl...
Accurate classification of focal liver lesions (FLLs) is crucial for reliable clinical decision-making. Inspired by contrastive vision-language models such as CLIP and MedCLIP, we propose Liver-VLM for FLLs classification, trained on a dedicated mult...
Rotating machinery is a key component in modern industry, and its operating condition directly affects equipment safety and production reliability. However, discrepancies among different machines cause source–target distribution shifts, while f...
This research introduces a novel method that integrates both unsupervised and supervised learning, leveraging SimCLR (Simple Framework for Contrastive Learning of Visual Representations) for self-supervised learning along with different pre-trained m...
Self-supervised contrastive learning has become an effective approach for visual representation learning when large-scale annotation is impractical. In this study, we evaluate three widely used methods—SimCLR, MoCo v2, and BYOL—for large-...
Finding a template location in a query image is a fundamental problem in many computer vision applications, such as localization of known objects, image registration, image matching, and object tracking. Currently available methods fail when insuffic...
Objective: This study aims to develop and validate an interpretable deep learning framework that leverages self-supervised time reversal (TR) pretraining to identify consistent, biologically plausible functional network biomarkers across multiple neu...
Early diagnosis of Alzheimer’s disease (AD) is an important task that facilitates the development of treatment and prevention strategies, and may potentially improve patient outcomes. Neuroimaging has shown great promise, including the amyloid-...
Soybean diseases are the main factors causing serious yield reduction in soybeans. Therefore, an automatic identification method for soybean leaf diseases is an urgent need to develop. However, most soybean leaf disease recognition models have proble...
Self-supervised learning can reduce the dependence of 3D medical image analysis on expensive voxel-level annotations, but masked image modeling remains inefficient when uniform random masking is applied to volumetric data with large redundant backgro...
Deep learning technology has been widely applied in emitter identification. With the deepening research, the problem of emitter identification under the few-shots condition has become a frontier research direction. As a special communication signal,...
Cotton fiber quality is shaped during boll development, boll opening, fluffing, and harvesting, but current assessment still relies largely on manual field inspection and postharvest laboratory testing. This limits timely harvest scheduling and plot-...
Electricity inconsistencies in mini-grids, stemming from meter drift, telemetry faults, topology misconfiguration, non-technical losses, phase imbalance or data manipulation, often emerge as weak, spatially distributed deviations that are difficult t...
As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks. This process requires numerous unlabeled training data and computational...
To address the limited data problem in real-world fault diagnosis, previous studies have primarily focused on semi-supervised learning and transfer learning methods. However, these approaches often struggle to obtain the necessary data, failing to fu...
Medical time series are sequential data collected over time that measures health-related signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive care unit (ICU) readings. Analyzing medical time series and identifying t...
The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional...
Diagnosing Parkinson’s disease (PD) through speech analysis is a promising area of research, as speech impairments are often one of the early signs of the disease. This study investigates the efficacy of fine-tuning pre-trained Automatic Speech...
Auditory research aims in general to lead to understanding of physiological processes. By contrast, the state of the art in automatic speech processing (notably recognition) is dominated by large pre-trained models that are meant to be used as black-...
Encrypted traffic now dominates IoT and sensor networks, and fully supervised classifiers are bottlenecked by label scarcity and the retraining cost of recognizing new attacks at the resource-constrained edge. TP-Net (Traffic Pretraining Network) cou...
Background/Objectives: Autism Spectrum Disorder (ASD) diagnosis is difficult due to heterogeneity. Current Time-series Transformer (TST) methods cannot capture both dynamic and global brain connectivity simultaneously, which limits ASD classification...
Facial motion representation learning has become an exciting research topic, since biometric technologies are becoming more common in our daily lives. One of its applications is identity verification. After recording a dynamic facial motion video for...
Medical image registration is a fundamental task in medical image analysis, enabling the alignment of images from different modalities or time points. However, intensity inconsistencies and nonlinear tissue deformations pose significant challenges to...
The air conditioning compressor serves as the primary power unit in refrigeration systems. Vibration data acquired from vibration sensors can be used to implement fault diagnosis, and reliable fault diagnosis is critical for ensuring safe and stable...
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