Results 71 to 80 of about 3,036,262 (193)

HDRSeg-UDA: Semantic Segmentation for HDR Images with Unsupervised Domain Adaptation

Smart Cities
Accurate detection and localization of traffic objects are essential for autonomous driving tasks such as path planning. While semantic segmentation is able to provide pixel-level classification, existing networks often fail under challenging conditions like nighttime or rain.
Huei-Yung Lin, Ming-Yiao Chen
openaire   +1 more source

Attention‐Guided Lightweight CNN‐Transformer Fusion for Real‐Time Traffic Sign Recognition in Adverse Environments: HACTNet

IET Intelligent Transport Systems, Volume 20, Issue 1, January/December 2026.
This paper proposes HACTNet, a low‐complexity CNN‐transformer hybrid model that pushes the state‐of‐art in TSR by making a noteworthy set of contributions including (i) efficient convaps to model parts of the image, (ii) transformer encoder to capture the global context and (iii) an attention‐based fusion block to dynamically combine the two ...
Mandeep Singh Devgan, Gurvinder Singh, Purushottam Sharma, Tajinder Kumar, Xiaochun Cheng, Deepak Ahlawat   +5 more
wiley   +1 more source

SEnsor Alignment for Multivariate Time-Series Unsupervised Domain Adaptation

, 2023
Unsupervised Domain Adaptation (UDA) methods can reduce label dependency by mitigating the feature discrepancy between labeled samples in a source domain and unlabeled samples in a similar yet shifted target domain.
Wang, Yucheng, Wu, Min, Yang, Jianfei, Li, Xiaoli, Xu, Yuecong, Xie, Lihua, Chen, Zhenghua   +6 more
core   +1 more source

Adversarial robustness for unsupervised domain adaptation

, 2021
Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.
ZHOU, F, Hong, L, Luo, P, LI, Z, Xu, H, Bae, SH, AWAIS, W   +6 more
core   +1 more source

Dual Frequency Side Scan Sonar Image Fusion for Deep‐Learning Based Underwater Target Detection

IET Radar, Sonar &Navigation, Volume 20, Issue 1, January/December 2026.
Traditional single‐frequency side‐scan sonar faces trade‐offs between imaging resolution and detection range, along with issues such as speckle noise and target–shadow coupling. To address these, this study proposes D2FNet, a dual‐domain fusion model integrating three key modules, which—tested on a new dataset of over 9000 paired sea‐trial images ...
Jiajun Xian, Liming Li, Fan Zhang, Yingying Wu, Weilin Zang, Lingji Xu, Hongxun Yao, Adam Rushworth, Johannes Michael Herrmann, Sanming Song   +9 more
wiley   +1 more source

EM-UDA: Emotion Detection Using Unsupervised Domain Adaptation for Classification of Facial Images

IEEE Access
Facial expressions can be used to interpret human feelings. They can be successfully used to assess the mood of a person. Accurate prediction of moods can prove to be of immense help in several areas including the mental health of an individual. Most methods proposed for facial emotion recognition use supervised learning.
Priti Rai Jain, S. M. K. Quadri, Anuradha Khattar   +2 more
openaire   +2 more sources

Source-free domain adaptation for semantic image segmentation using internal representations

Frontiers in Big Data
Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance.
Serban Stan, Mohammad Rostami
doaj   +1 more source

TF‐MEET: A Transferable Fusion Multi‐Band Transformer for Cross‐Session EEG Decoding

CAAI Transactions on Intelligence Technology, Volume 10, Issue 6, Page 1799-1812, December 2025.
ABSTRACT Electroencephalography (EEG) is a widely used neuroimaging technique for decoding brain states. Transformer is gaining attention in EEG signal decoding due to its powerful ability to capture global features. However, relying solely on a single feature extracted by the traditional transformer model to address the domain shift problem caused by ...
Qilong Yuan, Enze Shi, Di Zhu, Xiaoshan Zhang, Kui Zhao, Dingwen Zhang, Tianming Liu, Shu Zhang   +7 more
wiley   +1 more source

Unsupervised SAR Fine-Grained Ship Classification via Spherical Metric Refinement With Deep Subdomain Adaptation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Unsupervised domain adaptation (UDA) is a promising method for addressing the problem of SAR fine-grained ship classification in target domain with no labeled data available by leveraging a large number of labeled samples from source domains.
Zhichao Han, Haitao Lang
doaj   +1 more source

The SYNTHIDIA Dataset: Synthetic Insulator Defect Imaging and Annotation

High Voltage, Volume 10, Issue 6, Page 1496-1508, December 2025.
ABSTRACT Accurate and timely insulator defect detection is crucial for maintaining the reliability and safety of the power supply. However, the development of deep‐learning‐based insulator defect detection is hindered by the scarcity of comprehensive, high‐quality datasets for insulator defects.
Qingzhen Liu, Yadong Liu, Yingjie Yan, Qian Jiang, Xiuchen Jiang   +4 more
wiley   +1 more source