Maize is a globally major crop; however, the prevalence of mixed-aged seeds in the market complicates consumer selection and impedes the healthy development of the maize industry. This study introduces a novel method for identifying maize seeds of di...
Existing remote sensing images of ground objects are difficult to annotate, and building a hyperspectral dataset requires huge resources. To tackle these problems, this paper proposes a new method with low requirements for the scale of the dataset th...
This article conducts a systematic review on the fine-grained interpretation of remote sensing images, delving deeply into its background, current situation, datasets, methodology, and future trends, aiming to provide a comprehensive reference framew...
Due to their similar color and material variability, some ground objects have similar characteristics and overlap in some bands. This leads to a drop in the classification accuracy of hyperspectral images. To address this problem, we simulated hypers...
The Internet of Things (IoT) generates lots of high-dimensional sensor intelligent data. The processing of high-dimensional data (e.g., data visualization and data classification) is very difficult, so it requires excellent subspace learning algorith...
Existing facial expression recognition methods have some drawbacks. For example, it becomes difficult for network learning on cross-dataset facial expressions, multi-region learning on an image did not extract the overall image information, and a fre...
Recently, low-rank and sparse model-based dimensionality reduction (DR) methods have aroused lots of interest. In this paper, we propose an effective supervised DR technique named block-diagonal constrained low-rank and sparse-based embedding (BLSE)....
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....
In this paper, we propose a new dimensionality reduction method named Discriminative Sparsity Graph Embedding (DSGE) which considers the local structure information and the global distribution information simultaneously. Firstly, we adopt the intra-c...
Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract differe...
Vehicle re-identification holds great significance for intelligent transportation and public safety. Extracting vehicle recognition information from multi-view vehicle images has become one of the challenging problems in the field of vehicle recognit...
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...
Open set recognition (OSR) aims at dealing with unknown classes that are not included in the train set. However, existing OSR methods rely on deep learning networks that perform supervised learning on known classes in the train set, resulting in poor...
Synthetic aperture radar (SAR) image classification under limited data conditions faces two major challenges: inter-class similarity, where distinct radar targets (e.g., tanks and armored trucks) have nearly identical scattering characteristics, and...
Combining disease categories and crop species leads to complex intra-class and inter-class differences. Significant intra-class difference and subtle inter-class difference pose a great challenge to high-precision crop disease classification tasks. T...
Fine-grained few-shot image classification is a popular research area in deep learning. The main goal is to identify subcategories within a broader category using a limited number of samples. The challenge stems from the high intra-class variability...
With the growing emphasis on healthy eating and nutrition management in modern society, food image recognition has become increasingly important. However, it faces challenges such as large intra-class differences and high inter-class similarities. To...
Deep learning-based methods succeed in remote sensing scene classification (RSSC). However, current methods require training on a large dataset, and if a class does not appear in the training set, it does not work well. Zero-shot classification metho...
Neural style transfer, which has attracted great attention in both academic research and industrial engineering and demonstrated very exciting and remarkable results, is the technique of migrating the semantic content of one image to different artist...
Nowadays, the focus on few-shot object detection (FSOD) is fueled by limited remote sensing data availability. In view of various challenges posed by remote sensing images (RSIs) and FSOD, we propose a meta-learning-based Balanced Few-Shot Object Det...
Research has been growing on object detection using semi-supervised methods in past few years. We examine the intersection of these two areas for floor-plan objects to promote the research objective of detecting more accurate objects with less labele...
Semantic segmentation has emerged as a critical research area in Earth observation. This paper proposes a novel end-to-end semantic segmentation network, the Nested Cross-Scale and Bidirectional Feature Fusion Network (NCSBFF-Net), to address issues...
With the rapid development of deep learning, significant progress has been made in remote sensing image target detection. However, methods based on deep learning are confronted with several challenges: (1) the inherent limitations of activation funct...
The present study used the official Portuguese land use/land cover (LULC) maps (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict the spatiotemporal LULC transitions in the B...
Aiming at solving the problems of high background complexity of some butterfly images and the difficulty in identifying them caused by their small inter-class variance, we propose a new fine-grained butterfly classification architecture, called Netwo...
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...
The utilization of intermittent sampling jamming can engender a lofty verisimilitude false target cluster that exhibits coherence with the transmitted signal. Such an assemblage bears the hallmarks of both suppression jamming and deceitful jamming, c...
Facial expression recognition is crucial for understanding human emotions and nonverbal communication. With the growing prevalence of facial recognition technology and its various applications, accurate and efficient facial expression recognition has...
This study aims to capture subtle changes in the pupil, identify relatively weak inter-class changes, extract more abstract and discriminative pupil features, and study a pupil refinement recognition method based on attention mechanisms. Based on the...
The water supply pipeline system is responsible for providing clean drinking water to residents, but pipeline leaks can lead to water resource wastage, increased operational costs, and safety hazards. To effectively detect the leakage level in the wa...
Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automa...
Adversarial examples easily mislead vision systems based on deep neural networks (DNNs) trained with softmax cross entropy (SCE) loss. The vulnerability of DNN comes from the fact that SCE drives DNNs to fit on the training examples, whereas the resu...
Fine-grained image recognition is one of the key tasks in the field of computer vision. However, due to subtle inter-class differences and significant intra-class differences, it still faces severe challenges. Conventional approaches often struggle w...
Deep learning methods, especially convolutional neural networks (CNNs), have shown remarkable ability for remote sensing scene classification. However, the traditional training process of standard CNNs only takes the point-wise penalization of the tr...
Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue,...
Objects in images are characterized by intra-class variation, inter-class diversity, and noisy images. These characteristics pose a challenge to object localization. To address this issue, we present a novel joint Bayesian model for weakly-supervised...
Malicious traffic detection in IoT environments faces dual challenges: limited labeled data and heterogeneous, complex traffic patterns. To address these limitations, we propose a malicious traffic detection framework, GADF-SRGA, which integrates Gra...
Aiming at the problems of large intra-class differences, small inter-class differences, low contrast, and small and unbalanced datasets in dermoscopic images, this paper proposes a dermoscopic image classification method based on an ensemble of fine-...
With the development and popularization of unmanned aerial vehicles (UAVs) and surveillance cameras, vehicle re-identification (ReID) task plays an important role in the field of urban safety. The biggest challenge in the field of vehicle ReID is how...
Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have m...
Inflammatory bowel disease (IBD) includes Crohn’s disease (CD) and ulcerative colitis (UC). The accurate classification of IBD from colonoscopy images is critical for diagnosis and treatment. However, the lack of labeled data poses a major chal...
Acoustic scene classification (ASC) tries to inference information about the environment using audio segments. The inter-class similarity is a significant issue in ASC as acoustic scenes with different labels may sound quite similar. In this paper, t...
Recently, unsupervised domain adaptation (UDA) segmentation of remote sensing images (RSIs) has attracted a lot of attention. However, the performance of such methods still lags far behind that of their supervised counterparts. To this end, this pape...
Few-shot class incremental learning is a challenging problem in the field of machine learning. It necessitates models to gradually learn new knowledge from a few samples while retaining the knowledge of old classes. Nevertheless, the limited data ava...
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,...
Hyperspectral image (HSI) classification is a basic and significant task in remote sensing, the aim of which is to assign a class label to each pixel in an image. Recently, deep learning networks have been widely applied in HSI classification. They c...
This paper presents a new IndRNN-based deep attention model, termed DA-IndRNN, for skeleton-based action recognition to effectively model the fact that different joints are usually of different degrees of importance to different action categories. Th...
Fine-grained classifiers collect information about inter-class variations to best use the underlying minute and subtle differences. The task is challenging due to the minor differences between the colors, viewpoints, and structure in the same class e...
The identification of tomato leaf diseases is easily affected by complex backgrounds, small differences between different diseases, and large differences between the same diseases. Therefore, we propose a novel classification network for tomato leaf...
Flower classification is of great significance to the fields of plants, food, and medicine. However, due to the inherent inter-class similarity and intra-class differences of flowers, it is a difficult task to accurately classify them. To this end, t...
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