263 Results Found

Dataset bias is a significant obstacle that negatively affects image classification performance, especially in few-shot learning, where datasets have limited samples per class. However, few studies have focused on this issue. To address this, we prop...

Due to their compelling performance and appealing simplicity, metric-based meta-learning approaches are gaining increasing attention for addressing the challenges of few-shot image classification. However, many similar methods employ intricate networ...

Advances in deep learning can address a variety of computer vision problems. In particular, deep learning has shown high performance in image processing. However, large datasets are required to train deep learning models. Previous studies have addres...

Traditional deep learning methods such as convolutional neural networks (CNN) have a high requirement for the number of labeled samples. In some cases, the cost of obtaining labeled samples is too high to obtain enough samples. To solve this problem,...

In few-shot image classification (FSIC), the feature extraction module of the traditional convolutional neural networks is often constrained by the local nature of the convolutional kernel. As a result, it becomes challenging to handle global informa...

With the development of few-shot learning, significant progress has been achieved in hyperspectral image classification using related networks, leading to improved classification outcomes. However, practical few-shot hyperspectral image classificatio...

Few-shot learning, especially few-shot image classification (FSIC), endeavors to recognize new categories using only a handful of labeled images by transferring knowledge from a model trained on base categories. Despite numerous efforts to address th...

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and unde...

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....

Remote sensing image scene classification has become more and more popular in recent years. As we all know, it is very difficult and time-consuming to obtain a large number of manually labeled remote sensing images. Therefore, few-shot scene classifi...

Few-shot fine-grained image classification (FSFGIC) methods refer to the classification of images (e.g., birds, flowers, and airplanes) belonging to different subclasses of the same species by a small number of labeled samples. Through feature repres...

Few-shot image classification aims to classify unlabeled samples when only a small number of labeled samples are available for each class. Recently, local feature-based few-shot learning methods have made significant progress. However, existing metho...

Few-shot classification of polarimetric synthetic aperture radar (PolSAR) images is a challenging task due to the scarcity of labeled data and the complex scattering properties of PolSAR data. Traditional deep learning models often suffer from overfi...

Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote se...

Recently, many deep learning-based methods have been successfully applied to hyperspectral image (HSI) classification. Nevertheless, training a satisfactory network usually needs enough labeled samples. This is unfeasible in practical applications si...

In order to address the limitations of the number of label samples in practical accurate classification scenarios and the problems of overfitting and an insufficient generalization ability caused by Few-Shot Learning (FSL) in hyperspectral image clas...

For few-shot image classification tasks, the recognition accuracy of existing models remains limited due to the inherent complexity of the few-shot learning setting. To address this challenge, this paper proposes a few-shot image classification appro...

Image-based malware classification converts binary software into visual representations, enabling deep visual models to be used for malware family recognition. However, reliable family-level annotation remains labor-intensive and expert-dependent, ma...

Although the deep neural network (DNN) has shown a powerful ability in hyperspectral image (HSI) classification, its learning requires a large number of labeled training samples; otherwise, it is prone to over-fitting and has a poor classification pe...

In this research, inspired by the principles of biological visual attention mechanisms and swarm intelligence found in nature, we present an Enhanced Self-Correlation Attention and Multi-Branch Joint Module Network (EMNet), a novel model for few-shot...

Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spatial-domain features. Confined...

Deep neural networks have underpinned much of the recent progress in the field of hyperspectral image (HSI) classification owing to their powerful ability to learn discriminative features. However, training a deep neural network often requires the av...

In spite of recent rapid developments across various computer vision domains, numerous cutting-edge deep learning algorithms often demand a substantial volume of data to operate effectively. Within this research, a novel few-shot learning approach is...

Addressing challenges in few-shot image classification, this study introduces the Multi-Dimensional Attention and Composite Loss Former, a meta-learning model built on a Residual Network-12 backbone. The model incorporates multi-dimensional attention...

Few-shot fine-grained image classification (FSFGIC) aims to classify subspecies with similar appearances under conditions of very limited data. In this paper, we observe an interesting phenomenon: different types of image data augmentation techniques...

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...

Deep learning has been demonstrated to be a powerful nonlinear modeling method with end-to-end optimization capabilities for hyperspectral Images (HSIs). However, in real classification cases, obtaining labeled samples is often time-consuming and lab...

Few-shot image classification seeks to recognize novel categories from only a handful of labeled examples, but conventional metric-based methods that rely mainly on global image features often produce unstable prototypes under extreme data scarcity,...

Deep learning has flourished in large-scale supervised tasks. However, in many practical conditions, rich and available labeled data are a luxury. Thus, few-shot learning (FSL) has recently received boosting interest and achieved significant progress...

Remote sensing image scene classification (RSISC) has garnered significant attention in recent years. Numerous methods have been put forward in an attempt to tackle this issue, particularly leveraging deep learning methods that have shown promising p...

Few-shot image classification aims to recognize novel classes from only a handful of labeled examples, a challenge in domains where data collection is costly or impractical. Existing solutions often rely on meta learning, fine tuning, or data augment...

In recent years, few-shot fine-grained image classification has shown great potential in addressing data scarcity and distinguishing highly similar categories. However, existing unidirectional reconstruction methods, while enhancing inter-class diffe...

Complex-valued convolutional neural networks (CVCNNs) have demonstrated strong capabilities for polarimetric synthetic aperture radar (PolSAR) image classification by effectively integrating both amplitude and phase information inherent in polarimetr...

Recently, remote sensing image scene classification (RSISC) has gained considerable interest from the research community. Numerous approaches have been developed to tackling this issue, with deep learning techniques standing out due to their great pe...

Large vision-language models, such as Contrastive Vision-Language Pre-training (CLIP), pre-trained on large-scale image–text datasets, have demonstrated robust zero-shot transfer capabilities across various downstream tasks. To further enhance...

Fine-grained image classification is faced with the challenge of significant intra-class differences and subtle similarities between classes, with a limited number of labelled data. Previous few-shot learning approaches, however, often fail to recogn...

Hyperspectral image (HSI) classification in few-shot scenarios faces two core challenges. Limited samples and high spectral similarity lead to insufficient inter-class feature discriminability, and commonly used deep models suffer from the risk of ov...

Recognizing insect pests using images is an important and challenging research issue. A correct species classification will help choosing a more proper mitigation strategy regarding crop management, but designing an automated solution is also difficu...

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...

Few-shot fine-grained image classification aims to recognize unseen fine-grained categories from very few labeled examples. The task is difficult because visually similar classes may differ only in subtle details, while images from the same class can...

Fine-grained image classification under few-shot learning conditions remains a significant challenge due to limited labeled data and high intra-class similarity. This paper proposes a novel cross-modal framework that integrates Contrastive Language-I...

Over recent decades, the hyperspectral image (HSI) classification landscape has undergone significant transformations driven by advances in deep learning (DL). Despite substantial progress, few-shot scenarios remain a significant challenge, primarily...

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...

In hyperspectral image (HSI) classification scenarios, deep learning-based methods have achieved excellent classification performance, but often rely on large-scale training datasets to ensure accuracy. However, in practical applications, the acquisi...

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...

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...

Feature map reconstruction networks (FRN) have demonstrated significant potential by leveraging feature reconstruction. However, the typical process of FRN gives rise to two notable issues. First, FRN exhibits high sensitivity to noise, particularly...

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...

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...

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...