Recently, causal models have gained significant attention in natural language processing (NLP) and computer vision (CV) due to their capability of capturing features with causal relationships. This study addresses Fine-Grained Visual Categorization (...
Vegetable and fruit recognition can be considered as a fine-grained visual categorization (FGVC) task, which is challenging due to the large intraclass variances and small interclass variances. A mainstream direction to address the challenge is to ex...
In MOOC learning, learners’ emotions have an important impact on the learning effect. In order to solve the problem that learners’ emotions are not obvious in the learning process, we propose a method to identify learner emotion by combin...
Unsupervised person re-identification has attracted a lot of attention due to its strong potential to adapt to new environments without manual annotation, but learning to recognise features in disjoint camera views without annotation is still challen...
Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorpor...
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...
Multi-scale feature fusion techniques and covariance pooling have been shown to have positive implications for completing computer vision tasks, including fine-grained image classification. However, existing algorithms that use multi-scale feature fu...
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...
Named entity recognition (NER) is a natural language processing task to identify spans that mention named entities and to annotate them with predefined named entity classes. Although many NER models based on machine learning have been proposed, their...
The fine-grained image classification task is about differentiating between different object classes. The difficulties of the task are large intra-class variance and small inter-class variance. For this reason, improving models’ accuracies on t...
In smart classroom environments, accurately recognizing students’ facial expressions is crucial for teachers to efficiently assess students’ learning states, timely adjust teaching strategies, and enhance teaching quality and effectiveness. In this p...
Most existing intelligent editing tools for music and video rely on the cross-modal matching technology of the affective consistency or the similarity of feature representations. However, these methods are not fully applicable to complex audiovisual...
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...
In disease control and precision management in aquaculture, rapid and accurate identification of common fish diseases is pivotal to mitigating economic losses and ensuring aquaculture profitability. However, fish diseases are characterized by subtle...
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...
The quality of silkworm cocoons affects the quality and cost of silk processing. It is necessary to sort silkworm cocoons prior to silk production. Cocoon images consist of fine-grained images with large intra-class differences and small inter-class...
Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar...
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...
The introduction and application of the Vision Transformer (ViT) has promoted the development of fine-grained visual categorization (FGVC). However, there are some problems when directly applying ViT to FGVC tasks. ViT only classifies using the class...
Extracting entities from large volumes of chicken epidemic texts is crucial for knowledge sharing, integration, and application. However, named entity recognition (NER) encounters significant challenges in this domain, particularly due to the prevale...
With the flourishing development of corpus linguistics and technological revolutions in the AI-powered age, automated essay scoring (AES) models have been intensively developed. However, the intricate relationship between linguistic features and diff...
Unmanned aerial vehicle (UAV) aerial images often present challenges such as small target sizes, high target density, varied shooting angles, and dynamic poses. Existing target detection algorithms exhibit a noticeable performance decline when confro...
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 poisonous mushroom recognition in natural scenes is challenging because of complex backgrounds, subtle morphological differences, and the limited interpretability of model decisions. To address these challenges, this paper proposes Att-F...
Semantic change detection (SCD) is a newly important topic in the field of remote sensing (RS) image interpretation since it provides semantic comprehension for bi-temporal RS images via predicting change regions and change types and has great signif...
Event-based social network is a novel platform where users establish social relationships and realize interest matching through participation in offline events. However, event recommendation faces severe challenges, including extreme data sparsity du...
Fine-grained image retrieval aims at searching relevant images among fine-grained classes given a query. The main difficulty of this task derives from the small interclass distinction and the large intraclass variance of fine-grained images, posing s...
For daily activity recognition in smart homes, it is possible to reduce the effort required for labeling by transferring a trained model. This involves utilizing a labeled daily activity dataset from one smart home to recognize other activities in an...
This paper proposes a solution to the challenge of low accuracy in fine-grained vehicle classification, which arises from minimal intra-class feature variations. It introduces TS-Net, a multi-scale convolutional progressive fine-grained vehicle recog...
26 August 2022
Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clin...
For fine-grained recognition, capturing distinguishable features and effectively utilizing local information play a key role, since the objects of recognition exhibit subtle differences in different subcategories. Finding subtle differences between s...
Choroidal neovascularization (CNV) subtype classification from optical coherence tomography (OCT) images is clinically important because treatment response and disease prognosis vary across subtypes. However, automated classification remains challeng...
Semantic segmentation of high-resolution remote sensing images holds paramount importance in the field of remote sensing. To better excavate and fully fuse the features in high-resolution remote sensing images, this paper introduces a novel Global an...
Previous network feature extraction methods used for network anomaly detection have some problems, such as being unable to extract features from the original network traffic, or that they can only extract coarse-grained features, as well as that they...
In the field of remote sensing image captioning (RSIC), mainstream methods typically adopt an encoder–decoder framework. Methods based on this framework often use only simple feature fusion strategies, failing to fully mine the fine-grained fea...
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...
Accurate and robust visual localization under changing environments remains a fundamental challenge in autonomous driving and mobile robotics. Traditional handcrafted features often degrade under long-term illumination and viewpoint variations, while...
The focus of fine-grained image classification tasks is to ignore interference information and grasp local features. This challenge is what the visual attention mechanism excels at. Firstly, we have constructed a two-level attention convolutional net...
Fine-grained bird identification is crucial for ecosystem monitoring, species conservation, and habitat assessment. However, in real-world environments, there are challenges such as imbalances in modality quality and interference from background nois...
Cattle face segmentation and recognition in complex scenarios pose significant challenges due to insufficient fine-grained feature representation in segmentation networks and limited modeling of salient regions and local–global feature interact...
With the rapid development of energy transition and power system informatization, the efficient integration and feature migration of multimodal power data have become critical challenges for intelligent power systems. Existing methods often overlook...
Extracting fine-grained features from person images has proven crucial in person re-identification (re-ID). Although the research of convolutional neural networks (CNN) has been very successful in person re-ID, due to the small receptive field and do...
Air pollution presents a serious hazard to human health and the environment for the global rise in industrialization and urbanization. While fine-grained monitoring is crucial for understanding the formation and control of air pollution and their eff...
Multi-modal deep learning methods have achieved great improvements in visual grounding; their objective is to localize text-specified objects in images. Most of the existing methods can localize and classify objects with significant appearance differ...
The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for t...
Many current approaches for image classification concentrate solely on the most prominent features within an image, but in fine-grained image recognition, even subtle features can play a significant role in model classification. In addition, the larg...
Benefiting from the rapid development of geospatial big data-related technologies, intelligent transportation systems (ITS) have become a part of people’s daily life. Traffic volume forecasting is one of the indispensable tasks in ITS. The spat...
Fine-grained oriented object detection in remote sensing is challenged by extreme scale variation, arbitrary rotations, and dense layouts with structured clutter, where fixed feature selection and RoI extraction often lead to unstable localization an...
Existing architecture semantic modeling methods in 3D complex urban scenes continue facing difficulties, such as limited training data, lack of semantic information, and inflexible model processing. Focusing on extracting and adopting accurate semant...
With the development of advanced information and intelligence technologies, precision agriculture has become an effective solution to monitor and prevent crop pests and diseases. However, pest and disease recognition in precision agriculture applicat...
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