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...
Remote sensing image classification usually needs many labeled samples so that the target nature can be fully described. For synthetic aperture radar (SAR) images, variations of the target scattering always happen to some extent due to the imaging ge...
Cross-Domain Few-Shot Scene Classification (CDFSSC) aims to transfer knowledge from a source domain to a target domain for few-shot classification tasks, and is essential for remote sensing applications involving diverse platforms and dynamic environ...
Few-shot learning (FSL) is an effective solution for cross-domain hyperspectral image (HSI) classification, which could address the limited labeled samples of the target domain. Current FSL methods mostly utilize the 3D-CNN to transform the spatial a...
Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (H...
Cross-domain few-shot hyperspectral image (HSI) classification is challenging due to limited labeled samples and distribution shifts across sensors and acquisition scenes, which often degrade feature representation and classification performance. Thi...
The purpose of cross-domain sentiment classification (CDSC) is to fully utilize the rich labeled data in the source domain to help the target domain perform sentiment classification even when labeled data are insufficient. Most of the existing method...
The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain prod...
Remote sensing scene classification is one of the crucial techniques for high-resolution remote sensing image interpretation and has received widespread attention in recent years. However, acquiring high-quality labeled data is both costly and time-c...
Cross-domain few-shot hyperspectral image classification is challenged by scarce target-domain annotations, sensor-induced domain shifts, and spectral abnormalities, including band loss, noise, and spectral misalignment. To address these issues, we p...
In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much...
Sentiment classification often relies on costly in-domain annotations, while weak-supervision sources provide low-cost but inconsistent labels. We propose Multi-Source Reliability-Aware Aggregation and Cross-domain Consistency Learning (MSRA-CCL) for...
Accurate hyperspectral image analysis plays a critical role in environmental monitoring, precision agriculture, and urban mapping, yet acquiring large-scale annotated datasets for newly emerging scenes and sensors remains challenging. Cross-domain fe...
Classification of thyroid images based on the Bethesda category using Diff-Quick stained images can assist in diagnosing thyroid cancer. This paper proposes a cross-domain approach that modifies the original deep learning network designed to classify...
Cross-domain semantic classification of 3D point clouds remains challenging due to strong domain shifts between heterogeneous sensing modalities. Most existing classification frameworks are domain-specific, limiting their use in integrated land&ndash...
The categorization of real-time defects in heterogeneous domains is a long-standing challenge in the field of industrial visual inspection systems, primarily due to significant visual variations and the lack of labelled information in real-world insp...
To address the problem of the expensive and time-consuming annotation of high-resolution remote sensing images (HRRSIs), scholars have proposed cross-domain scene classification models, which can utilize learned knowledge to classify unlabeled data s...
Scene classification on remote sensing imagery is usually based on supervised learning but collecting labelled data in remote sensing domains is expensive and time-consuming. Bag of Visual Words (BOVW) achieves great success in scene classification b...
Multi-source domain adaptation (MSDA) in remote sensing (RS) scene classification has recently gained significant attention in the visual recognition community. It leverages multiple well-labeled source domains to train a model capable of achieving s...
By labelling high spatial resolution (HSR) images with specific semantic classes according to geographical properties, scene classification has been proven to be an effective method for HSR remote sensing image semantic interpretation. Deep learning...
Hyperspectral sensors provide high-dimensional spectral–spatial observations for land-cover analysis, but reliable classification remains difficult when only a few target-scene labels are available. Cross-domain few-shot hyperspectral image cla...
Cross-domain few-shot learning (CD-FSL) remains challenging in medical imaging, where labeled data are scarce and source–target domain gaps are often large due to modality differences. In particular, existing few-shot learning methods rely on s...
Learners in a massive open online course often express feelings, exchange ideas and seek help by posting questions in discussion forums. Due to the very high learner-to-instructor ratios, it is unrealistic to expect instructors to adequately track th...
Domain adaptation for classification has achieved significant progress in natural images but not in remote-sensing images due to huge differences in data-imaging mechanisms between different modalities and inconsistencies in class labels among existi...
Cross-scene hyperspectral image (HSI) classification seeks to learn a classifier from annotated source-scene data and deploy it on unlabeled scenes whose imaging conditions and data distributions differ from those seen during training. Existing cross...
Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness i...
Cross-domain scene classification requires the transfer of knowledge from labeled source domains to unlabeled target domain data to improve its classification performance. This task can reduce the labeling cost of remote sensing images and improve th...
In recent years, cross-domain few-shot learning (CDFSL) has demonstrated remarkable performance in hyperspectral image classification (HSIC), partially alleviating the distribution shift problem. However, most domain adaptation methods rely on simila...
Rapid and reliable tea classification is valuable for routine product screening, yet conventional sensory or physicochemical methods are subjective or time-consuming. Electronic nose (E-nose) sensing provides a fast alternative, but performance often...
Natural scene classification, which has potential applications in precision agriculture, environmental monitoring, and disaster management, poses significant challenges due to variations in the spatial resolution, spectral resolution, texture, and si...
Acoustic vehicle classification from roadside microphones supports source-specific traffic noise monitoring, but classifiers trained on one dataset generalize poorly to recordings from different environments. This study compares pre-trained audio tra...
Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (ta...
Transfer learning is one of the popular methods for solving the problem that the models built on the source domain cannot be directly applied to the target domain in the cross-domain sentiment classification. This paper proposes a transfer learning m...
The proliferation of fake news poses a significant challenge in today’s information landscape, spanning diverse domains and topics and undermining traditional detection methods confined to specific domains. In response, there is a growing inter...
To address the growing congestion of the radio frequency (RF) spectrum, Cognitive Radio (CR) systems employ Automatic Modulation Classification (AMC) to dynamically optimize spectrum utilization without introducing protocol overhead. In modern Orthog...
This paper introduces the Wavelet-Enhanced Swin Transformer Network (WSC-Net), a novel dual-branch architecture that resolves the inherent tradeoff between global spatial contextual and fine-grained spectral details in hyperspectral image (HSI) class...
Cross-domain ground-based cloud classification is a challenging issue as the appearance of cloud images from different cloud databases possesses extreme variations. Two fundamental problems which are essential for cross-domain ground-based cloud clas...
Multi-source cross-domain hyperspectral image (HSI) classification is challenged by heterogeneous sensor configurations, scene-dependent distribution shifts, and limited labeled target data, which hinder effective knowledge transfer across multiple s...
Ultrasound imaging is widely used for early breast cancer screening to enhance patient survival. However, interpreting these images is inherently challenging due to speckle noise, low lesion-to-tissue contrast, and highly variable tumor morphology wi...
Transformer-based fake news classifiers often perform well within their training domain but show substantial performance degradation when applied to a different domain. This degradation is commonly attributed to changes in learned representations, wh...
With the development of deep learning and sensors and sensor collection methods, computer vision inspection technology has developed rapidly. The deep-learning-based classification algorithm requires the acquisition of a model with superior generaliz...
Domain shift poses a major challenge in many Machine Learning applications due to variations in data acquisition protocols, particularly in the medical field. Test-time augmentation (TTA) can solve the domain shift issue and improve robustness by agg...
With the increasing availability of satellite imagery and the shortening revisit intervals, efficiently processing satellite hyperspectral images has become a critical task. However, in practice, a large portion of satellite hyperspectral data remain...
With the advancement of artificial intelligence, Agricultural Expert Systems (AESs) show great potential in enhancing agricultural management efficiency and resource utilization. Accurate extraction of semantic features from agricultural short texts...
Leaf disease classifiers are ranked by held-out accuracy on their training corpus, where that number now exceeds 98% for many modern backbones. We ask what such a measurement can still resolve. On a four-crop, 21-class corpus of 7179 de-duplicated im...
Compost maturity screening supports safe land application and organic waste recycling, but germination index (GI) assays are not always available for rapid process assessment. This study performed a secondary GI-based maturity classification reconstr...
Few-shot hyperspectral image classification (FS-HSIC) remains highly challenging because the limited number of labeled samples not only makes traditional supervised learning models prone to overfitting but also restricts their ability to learn genera...
Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-sess...
Cross-scene classification of hyperspectral images poses significant challenges due to the lack of a priori knowledge and the differences in data distribution across scenes. While traditional studies have had limited use of a priori knowledge from ot...
Background: Domain adaptation (DA) techniques have emerged as a pivotal strategy in addressing the challenges of cross-subject classification. However, traditional DA methods are inherently limited by the assumption of a homogeneous space, requiring...
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