Recently, time-series forecasting foundation models trained on large, diverse datasets have demonstrated robust zero-shot and few-shot capabilities. Given the ubiquity of time-series data in IoT, finance, and industrial applications, rigorous benchma...
Automated waste classification is an essential step toward efficient recycling and waste management. Traditional deep learning models, such as convolutional neural networks, rely on extensive labeled datasets to achieve high accuracy. However, the an...
Existing zero-shot learning (ZSL) methods typically focus on mapping from the feature space (e.g., visual space) to class-level attributes, often leading to a non-injective projection. Such a mapping may cause a significant loss of instance-level inf...
Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize...
There are many areas where conventional supervised machine learning does not work well, for instance, in cases with a large, or systematically increasing, number of countably infinite classes. Zero-shot learning has been proposed to address this. In...
Few-shot learning (FSL) and zero-shot learning (ZSL) are usually studied as separate problems, yet both require prediction when class-specific evidence is absent or scarce. This review analyzes their shared difficulty from an optimization perspective...
With the rise of online advertising, e-commerce industries, and new media platforms, recommendation systems have become an essential product form that connects users with a vast number of candidates. A major challenge in recommendation systems is the...
Relation extraction serves as an essential task for knowledge acquisition and management, defined as determining the relation between two annotated entities from a piece of text. Over recent years, zero-shot learning has been introduced to train rela...
Zero-shot semantic segmentation (ZS3), the process of classifying unseen classes without explicit training samples, poses a significant challenge. Despite notable progress made by pre-trained vision-language models, they have a problem of “supe...
Generalized zero-shot learning (GZSL) aims to solve the category recognition tasks for unseen categories under the setting that training samples only contain seen classes while unseen classes are not available. This research is vital as there are alw...
Accurate classification of brain tumors from MRI scans remains challenging due to limited annotated data. This study compares data-efficient paradigms—few-shot learning (FSL) and zero-shot learning (ZSL)—for tumor diagnosis using deep lea...
Traditional segmentation methods are slow and rely on manual annotations, which are labor-intensive. To address these limitations, we propose YOLO-SAM AgriScan, a unified framework that combines the fast object detection capabilities of YOLOv11 with...
Zero-shot counting is a subcategory of Generic Visual Object Counting, which aims to count objects from an arbitrary class in a given image. While few-shot counting relies on delivering exemplars to the model to count similar class objects, zero-shot...
Prompt engineering has emerged as a practical and resource-efficient alternative to fine-tuning large language models (LLMs), particularly as these methods have a lower computation cost than fine-tuning. In this paper, three widely adopted prompting...
Topic classification is the task of mapping text onto a set of meaningful labels known beforehand. This scenario is very common both in academia and industry whenever there is the need of categorizing a big corpus of documents according to set custom...
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...
Traditional machine learning methods only classify the instances whose classes are seen during training. In practice, many applications require to recognize the classes unknown in the training stage. In order to tackle this kind of challenging task,...
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...
In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learnin...
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...
In this paper, we address the task of zero-shot cross-lingual news sentiment classification. Given the annotated dataset of positive, neutral, and negative news in Slovene, the aim is to develop a news classification system that assigns the sentiment...
Zero-shot neural decoding aims to decode image categories, which were not previously trained, from functional magnetic resonance imaging (fMRI) activity evoked when a person views images. However, having insufficient training data due to the difficul...
Zero-shot learning (ZSL) is implemented by transferring knowledge from seen classes to unseen classes through embedding space or feature generation. However, the embedding-based method has a hubness problem, and the generation-based method may contai...
Zero-shot learning (ZSL) enables the recognition of unseen categories by leveraging models trained only on labeled seen-class samples in the source domain. Traditional ZSL methods typically assume identical data distributions across source and target...
The digitization of healthcare has been accompanied by a rapid expansion of electronic health records (EHRs); however, a significant proportion of critical patient data, specifically medication regimens, remains entrapped within unstructured clinical...
In social networks, the occurrence of unexpected events rapidly catalyzes the widespread dissemination and further evolution of network public opinion. The advent of zero-shot stance detection aligns more closely with the characteristics of stance de...
Zero-shot learning (ZSL) holds significant promise for scaling image classification to previously unseen classes by leveraging previously acquired knowledge. However, conventional ZSL methods face challenges such as domain-shift and hubness problems....
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and re...
The supervised model based on deep learning has made great achievements in the field of image classification after training with a large number of labeled samples. However, there are many categories without or only with a few labeled training samples...
Zero-shot learning (ZSL) enables models to recognize categories not encountered during training, which is crucial for categories with limited data. Existing methods overlook efficient temporal modeling in multimodal data. This paper proposes a Tempor...
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...
Conventional zero-shot learning aims to train a classifier on a training set (seen classes) to recognize instances of novel classes (unseen classes) by class-level semantic attributes. In generalized zero-shot learning (GZSL), the classifier needs to...
This study presents a zero-shot object detection framework for corner casting detection in shipping container operations, leveraging edge computing for intelligent robotic perception and control. The proposed system integrates Grounding DINO on a Ras...
Zero-shot learning (ZSL) aims to categorize target classes with the aid of semantic knowledge and samples from previously seen classes. In this process, the alignment of visual and attribute modality features is key to successful knowledge transfer....
With the proliferation of mobile devices, identifying previously unseen mobile applications has become a critical challenge in network security. Traditional application identification approaches rely heavily on fixed training categories and limited t...
With the rapid proliferation of large language models (LLMs), distinguishing machine-generated text from human-authored content has become increasingly critical for ensuring content authenticity, academic integrity, and trust in information systems....
Natural sand-dust weather is complicated, and synthetic sand-dust datasets cannot accurately reflect the properties of real sand-dust images. Sand-dust image enhancement and restoration methods that are based on enhancement, on priors, or on data-dri...
Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of deep learning, so...
Zero-shot stance detection aims to identify the stance expressed in social media text aimed at specific targets without relying on annotated data. However, due to insufficient contextual information and the inherent ambiguity of language, this task f...
Stance detection aims to identify whether a text expresses a favorable, opposing, or neutral attitude toward a given target and has become increasingly important for analyzing public discourse on social media. Existing approaches, ranging from superv...
Sonar is a valuable tool for ocean exploration since it can obtain a wealth of data. With the development of intelligent technology, deep learning has brought new vitality to underwater sonar image classification. However, due to the difficulty and h...
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...
Zero-shot classification presents a challenge since it necessitates a model to categorize images belonging to classes it has not encountered during its training phase. Previous research in the field of remote sensing (RS) has explored this task by tr...
Although low-resource relation extraction is vital in knowledge construction and characterization, more research is needed on the generalization of unknown relation types. To fill the gap in the study of low-resource (Uyghur) relation extraction meth...
Low-light images often suffer from insufficient illumination and noise, but enhancing dark regions can further amplify noise and degrade texture and structural details. To address these challenges, we propose Zero-MS, a zero-shot method for low-light...
Zero-cost proxies reduce the cost of neural architecture search (NAS), the automated design of neural network architectures, by scoring untrained candidate architectures without full training. Although this makes individual evaluations inexpensive, z...
Traffic sign recognition is a complex and challenging yet popular problem that can assist drivers on the road and reduce traffic accidents. Most existing methods for traffic sign recognition use convolutional neural networks (CNNs) and can achieve hi...
This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventiona...
Multimodal question answering (QA) involves integrating information from both visual and textual inputs and requires models that can reason compositionally and accurately across modalities. Existing approaches, including fine-tuned vision–langu...
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...
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