Emerging deep learning-based fault diagnosis methods have advanced in the current industrial scenarios of various working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical en...
In the real industrial manufacturing process, due to the constantly changing operational loads of equipment, it is difficult to collect data from all load conditions as the source domain signal for fault diagnosis. Therefore, the appearance of unseen...
How can human feedback be effectively integrated into generative models? This study addresses this question by proposing a method to enhance image denoising and achieve domain adaptation using human feedback. Deep generative models, while achieving r...
In recent years, domain generalization-based fault diagnosis (DGFD) methods have shown significant potential in rotating machinery fault diagnosis in unseen target domains. However, these methods focus on learning domain-invariant representations via...
Gender recognition from pedestrian imagery is acknowledged by many as a quasi-solved problem, yet most existing approaches evaluate performance in a within-domain setting, i.e., when the test and training data, though disjoint, closely resemble each...
Satellite imagery segmentation is essential for effective land resource management. However, diverse geographical landscapes may limit segmentation accuracy in practical applications. To address these challenges, we propose the F-Segformer network, w...
Speech Activity Detection (SAD) aims to accurately classify audio fragments containing human speech. Current state-of-the-art systems for the SAD task are mainly based on deep learning solutions. These applications usually show a significant drop in...
Data-driven fault diagnosis has received significant attention in the era of big data. Most data-driven methods have been developed under the assumption that both training and test data come from identical data distributions. However, in real-world i...
In practical engineering, the asymmetric problem of the domain label space is inevitable owing to the prior fault information of the target domain being difficult to completely obtain. This implies that the target domain may include unseen fault clas...
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....
The segmentation of the left ventricle endocardium (LVendo) and the left ventricle epicardium (LVepi) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have been the most commonly used approach for echo...
Few-shot learning aims to identify unseen classes with limited labelled data. Recent few-shot learning techniques have shown success in generalizing to unseen classes; however, the performance of these techniques has also been shown to degrade when t...
RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, exi...
Applying pre-trained medical deep learning segmentation models to out-of-domain images often yields predictions of insufficient quality. In this study, we propose using a robust generalizing descriptor, along with augmentation, to enable domain-gener...
Due to significant advances in sensor technology, studies towards activity recognition have gained interest and maturity in the last few years. Existing machine learning algorithms have demonstrated promising results by classifying activities whose i...
Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely...
Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a large number of repetitive planar structures, the point...
Deep learning methods provide significant assistance in analyzing coronavirus disease (COVID-19) in chest computed tomography (CT) images, including identification, severity assessment, and segmentation. Although the earlier developed methods address...
This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspective...
Domain generalization (DG) aims to develop models that perform robustly on unseen target domains, a critical but challenging objective for real-world fault diagnosis. The challenge is further complicated in compound fault diagnosis, where the rigidit...
Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent researc...
We present the main messages of a European Expert Round Table (ERT) on the unintended side effects (unseens) of the digital transition. Seventeen experts provided 42 propositions from ten different perspectives as input for the ERT. A full-day ERT de...
Standard Deep Learning-based detectors generally face a trade-off between accuracy and latency, as well as a significant performance degradation when applied to unseen environments. To address these challenges, this study proposes a method that enhan...
Text2Cypher is a text-to-text task that converts natural language questions into Cypher queries. Recent research by Neo4j on Text2Cypher demonstrates that fine-tuning a baseline language model (a pretrained and instruction-tuned generative model) usi...
Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As...
In order to deal with the cross-domain distribution offset problem in mechanical fault diagnosis under different operating conditions. Domain-adaptive (DA) methods, such as domain adversarial neural networks (DANNs), maximum mean discrepancy (MMD), a...
In the thermal industry, one common way to transfer heat between hot tubes and cooling fluid is using cross-flow heat exchangers. For heat exchangers, microscale coatings are conventional safeguards for tubes from corrosion and dust accumulation. Thi...
The zero-shot image classification (ZSIC) is designed to solve the classification problem when the sample is very small, or the category is missing. A common method is to use attribute or word vectors as a priori category features (auxiliary informat...
Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computat...
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...
Predicting biological properties of unseen proteins is shown to be improved by the use of protein sequence embeddings. However, these sequence embeddings have the caveat that biological metadata do not exist for each amino acid, in order to measure t...
Speech emotion recognition (SER) is a natural method of recognizing individual emotions in everyday life. To distribute SER models to real-world applications, some key challenges must be overcome, such as the lack of datasets tagged with emotion labe...
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, p...
Domain generalization (DG) has become a pivotal research area in machine learning, focusing on equipping models with the ability to generalize effectively to unseen test domains that differ from the training distribution. This capability is crucial,...
Named entity recognition (NER) in few-shot scenarios plays a critical role in entity annotation for low-resource domains. However, existing methods are often limited to learning semantic features and intermediate representations specific to the sourc...
Intent recognition is a key component of any task-oriented conversational system. The intent recognizer can be used first to classify the user’s utterance into one of several predefined classes (intents) that help to understand the user’s...
Background/Objectives: Artificial intelligence (AI) is revolutionising healthcare for people with disabilities, including those with autism spectrum disorder (ASD), in the era of advanced technology. This work explicitly addresses the challenges pose...
Forest operations often expose workers to physical risks, including posture-related disorders such as low back pain. The Ovako Working Posture Assessment System (OWAS) is widely used to assess postures in forest operations, but it requires expertise...
Automated target recognition is an important task in the littoral warfare domain, as distinguishing mundane objects from mines can be a matter of life and death. This is initial work towards the application of convolutional autoencoding to the littor...
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....
Due to the existence of domain shift, the synthetic aperture radar (SAR) automatic target recognition (ATR) model trained on simulated data will have significant performance degradation when applied to real-world measured data. To bridge the domain g...
Although the means of catching remote sensing images are becoming more effective and more abundant, the samples that can be collected in some specific environments can be quite scarce. When there are limited labeled samples, the methods for analyzing...
Electrocardiography (ECG)-based arrhythmia classification intends to have a massive role in cardiovascular disease monitoring and early diagnosis. However, ECG datasets are mostly imbalanced and have regularization to use real-time patient data due t...
The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes....
The aim of this study is to improve the cross-condition domain adaptability of bearing fault diagnosis models and their diagnostic performance under previously unknown conditions. Thus, this paper proposes a multi-condition adaptive bearing fault dia...
In this study, we present a novel concept termed open-vocabulary domain generalization (OVDG), which we investigate within the context of semantic segmentation. OVDG presents greater difficulty compared to conventional domain generalization, yet it o...
In-car activity monitoring is a key enabler of various automotive safety functions. Existing approaches are largely based on vision systems. Radar, however, can provide a low-cost, privacy-preserving alternative. To this day, such systems based on th...
Speech synthesis is a technology that converts text into speech waveforms. With the development of deep learning, neural network-based speech synthesis technology is being researched in various fields, and the quality of synthesized speech has signif...
Advanced deception countermeasures now enable adversaries to inject false targets into synthetic-aperture-radar (SAR) imagery, generating electromagnetic signatures virtually indistinguishable from genuine targets, thus destroying the separability es...
Earth-observation-based mapping plays a critical role in humanitarian responses by providing timely and accurate information in inaccessible areas, or in situations where frequent updates and monitoring are required, such as in internally displaced p...
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