This research presents a retrospective analysis of zero-shot object detectors in automating image labeling for eyeglasses detection. The increasing demand for high-quality annotations in object detection is being met by AI foundation models with open...
The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been publ...
Zero-Shot Learning (ZSL) is related to training machine learning models capable of classifying or predicting classes (labels) that are not involved in the training set (unseen classes). A well-known problem in Deep Learning (DL) is the requirement fo...
Species recognition is a crucial part of understanding the abundance and distribution of various organisms and is important for biodiversity conservation and management. Traditional vision-based deep learning-driven species recognition requires large...
The zero-shot image classification technique aims to explore the semantic information shared between seen and unseen classes through visual features and auxiliary information and, based on this semantic information, to complete the knowledge migratio...
Automated food safety inspection systems rely heavily on the visual detection of contamination, spoilage, and foreign objects in food products. Current approaches typically require extensive labeled training data for each specific hazard type, limiti...
Video classification is a challenging task in computer vision that requires analyzing the content of a video to assign it to one or more predefined categories. However, due to the vast amount of visual data contained in videos, the classification pro...
With the rapid developments of hyperspectral imaging, the cost of collecting hyperspectral data has been lower, while the demand for reliable and detailed hyperspectral annotations has been much more substantial. However, limited by the difficulties...
Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classific...
This paper presents Hy-LIFT (Hybrid LLM-Integrated Fault Diagnosis Toolkit), a multi-stage framework for interpretable and data-efficient fault diagnosis in 5G/6G networks that integrates a high-precision interpretable rule-based engine (IRBE) for kn...
This study introduces a novel AI-driven approach to support elderly patients in Thailand with medication management, focusing on accurate drug label interpretation. Two model architectures were explored: a Two-Stage Optical Character Recognition (OCR...
24 September 2025
Accurate gender identification from written text is critical for author profiling, recommendation systems, and demographic analytics in digital ecosystems. This study introduces a scalable framework for gender classification in Turkish, combining con...
Two challenges in computer vision (CV) related to face detection are the difficulty of acquisition in the target domain and the degradation of image quality. Especially in low-light situations, the poor visibility of images is difficult to label, whi...
Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely...
In computer vision, the performance of detectors trained under optimal lighting conditions is significantly impaired when applied to infrared domains due to the scarcity of labeled infrared target domain data and the inherent degradation in infrared...
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...
Compositional Zero-Shot Learning (CZSL) seeks to enable machines to recognize objects and attributes (i.e., primitives),learn their associations, and generalize to novel compositions, enabling systems to exhibit a human-like ability to infer and gene...
With the growing demand for labeled textual data in Natural Language Processing (NLP), traditional data collection and annotation methods face significant challenges, such as high cost, limited scalability, and privacy constraints. This study present...
Emotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emoti...
In traffic safety analysis, previous research has often focused on tabular data or textual crash narratives in isolation, neglecting the potential benefits of a hybrid multimodal approach. This study introduces the Multimodal Data Fusion (MDF) framew...
Visual relationship detection (VRD), a challenging task in the image understanding, suffers from vague connection between relationship patterns and visual appearance. This issue is caused by the high diversity of relationship-independent visual appea...
Recently, the focus of entity linking research has centered on the zero-shot scenario, where the entity purposed to be labeled at the time of testing was never observed during the training phase, or it may belong to a different domain than the source...
Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media,...
Object detection is a crucial research topic in the fields of computer vision and artificial intelligence, involving the identification and classification of objects within images. Recent advancements in deep learning technologies, such as YOLO (You...
The contrastive vision–language pre-trained model CLIP, driven by large-scale open-vocabulary image–text pairs, has recently demonstrated remarkable zero-shot generalization capabilities in diverse downstream image tasks, which has made n...
The proliferation of digital public service platforms and the expansion of e-government initiatives have significantly increased the volume and diversity of citizen-generated feedback. This trend emphasizes the need for classification systems that ar...
To obtain actionable information for humanitarian and other emergency responses, an accurate classification of news or events is critical. Daily news and social media are hard to classify based on conveyed information, especially when multiple catego...
Most supervised classification methods for polarimetric synthetic aperture radar (PolSAR) data rely on abundant labeled samples, and cannot tackle the problem that categorizes or infers unseen land cover classes without training samples. Aiming to ca...
The research presented in the paper aims at increasing the capacity to identify security weaknesses in programming languages that are less supported by specialized security analysis tools, based on the knowledge gathered from securing the popular one...
Market indices, such as the S&P 500, serve as compressed representations of complex constituent-level dynamics. This study proposes a zero-shot forecasting framework capable of predicting index-level trajectories without direct supervision from i...
Limited labeled imagery and tight response windows hinder the accurate damage quantification for post-disaster assessment. The objective of this study is to develop and evaluate a deep learning-based Domain-Adaptive Segmentation (DASeg) workflow to d...
Social media platforms have surpassed cultural and linguistic boundaries, thus enabling online communication worldwide. However, the expanded use of various languages has intensified the challenge of online detection of hate speech content. Despite t...
Similarity learning using deep convolutional neural networks has been applied extensively in solving computer vision problems. This attraction is supported by its success in one-shot and zero-shot classification applications. The advances in similari...
Timely detection of road surface defects such as cracks and potholes is critical for ensuring traffic safety and reducing infrastructure maintenance costs. While recent advances in image-based deep learning techniques have shown promise for automated...
Traditional fault diagnosis methods for rolling bearings rely on nemerous labeled samples, which are difficult to obtain in engineering applications. Moreover, when unseen fault categories appear in the test set, these models fail to achieve accurate...
Although image recognition technologies are developing rapidly with deep learning, conventional recognition models trained by supervised learning with class labels do not work well when test inputs from untrained classes are given. For example, a rec...
Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we prop...
Pavement condition assessment using computer vision has emerged as an efficient alternative to traditional manual surveys, which are often labor-intensive and time-consuming. Leveraging deep learning, pavement distress such as cracks can be automatic...
High-resolution range profile (HRRP) target recognition has garnered significant attention in radar automatic target recognition (RATR) research for its rich structural information and low computational costs. With the rapid advancements in deep lear...
Human-to-human dialogues constitute an essential research area for linguists, serving as a conduit for knowledge transfer in the study of dialogue systems featuring human-to-machine interaction. Dialogue systems have garnered significant acclaim and...
This paper presents an integrated three-dimensional (3D) quality inspection system for mold manufacturing that addresses critical industrial constraints, including zero-shot generalization without retraining, complete decision traceability for regula...
Supervised training has traditionally been the cornerstone of hate speech detection models, but it often falls short when faced with unseen scenarios. Zero and few-shot learning offers an interesting alternative to traditional supervised approaches....
Robotic search of people in human-centered environments, including healthcare settings, is challenging, as autonomous robots need to locate people without complete or any prior knowledge of their schedules, plans, or locations. Furthermore, robots ne...
Root-cause analysis (RCA) in large-scale microservice-based payment systems is challenging due to complex failure propagation along service dependencies, limited availability of labeled incident data, and heterogeneous service topologies across deplo...
Medical anomaly detection is challenged by limited labeled data and domain shifts, which reduce the performance and generalization of deep learning (DL) models. Hybrid convolutional neural network–Vision Transformer (CNN–ViT) architecture...
Background and Objectives: Type 2 diabetes mellitus (T2DM) affects over 537 million adults worldwide and disproportionately burdens low- and middle-income countries, where diagnostic resources are limited. Predictive models trained in one population...
Social media serves as a platform for netizens to stay informed and express their opinions through the Internet. Currently, the social media discourse environment faces a significant security threat—offensive comments. A group of users posts co...
Drug abuse is a serious problem in the United States, with over 90,000 drug overdose deaths nationally in 2020. A key step in combating drug abuse is detecting, monitoring, and characterizing its trends over time and location, also known as pharmacov...