Zobrazeno 1 - 10
of 491
pro vyhledávání: '"supervised contrastive learning"'
Publikováno v:
Nano-Micro Letters, Vol 17, Iss 1, Pp 1-15 (2024)
Highlights Utilizing self-supervised learning, the proposed wearable wristband with a four-channel sensing array and wireless transmission module is developed for tracking air-writing and dynamic gestures. The model can learn prior features from unla
Externí odkaz:
https://doaj.org/article/e1fdb8ae2dd4417692e4dac070b4c2ad
Multidomain Characteristic-guided Multimodal Contrastive Recognition Method for Active Radar Jamming
Publikováno v:
Leida xuebao, Vol 13, Iss 5, Pp 1004-1018 (2024)
Achieving robust joint utilization of multidomain characteristics and deep-network features while maintaining a high jamming-recognition accuracy with limited samples is challenging. To address this issue, this paper proposes a multidomain characteri
Externí odkaz:
https://doaj.org/article/e9281dce7d244213b75f73b89229c604
Publikováno v:
Big Data Mining and Analytics, Vol 7, Iss 3, Pp 765-780 (2024)
Understanding the subcellular localization of long non-coding RNAs (lncRNAs) is crucial for unraveling their functional mechanisms. While previous computational methods have made progress in predicting lncRNA subcellular localization, most of them ig
Externí odkaz:
https://doaj.org/article/422169b76ccb4e3a9369e1499a6aa3ee
Publikováno v:
International Journal of Computational Intelligence Systems, Vol 17, Iss 1, Pp 1-13 (2024)
Abstract Schizophrenic patients’ brain tumor magnetic resonance imaging (MRI) images are important references for doctors to diagnose and treat schizophrenia. However, automatic segmentation of these images is a professional and tedious task. Exist
Externí odkaz:
https://doaj.org/article/97836a1b97844ee7854d9fb6b130dff7
Accurate pneumoconiosis staging via deep texture encoding and discriminative representation learning
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
Accurate pneumoconiosis staging is key to early intervention and treatment planning for pneumoconiosis patients. The staging process relies on assessing the profusion level of small opacities, which are dispersed throughout the entire lung field and
Externí odkaz:
https://doaj.org/article/2bf9d6b497da45a184ddb012bea41cc5
Publikováno v:
IEEE Open Journal of the Computer Society, Vol 5, Pp 660-670 (2024)
Deception detection is essential for protecting the public interest and maintaining social order. Its application in various fields helps to establish a safer and trustworthy social environment. This study focuses on the problem of deception detectio
Externí odkaz:
https://doaj.org/article/f1a031ff2bf1411bada16fe887042bb0
Autor:
Bo Wang, Tsunenori Mine
Publikováno v:
IEEE Access, Vol 12, Pp 148488-148501 (2024)
This paper presents a novel and comprehensive solution to enhance both the robustness and efficiency of question answering (QA) systems through supervised contrastive learning (SCL). Training a high-performance QA system has become straightforward wi
Externí odkaz:
https://doaj.org/article/7e2a86814d714f35ba48f3b61cceda5a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17710-17724 (2024)
Self-supervised contrastive learning can help alleviating the meet of large numbers of annotated samples and learning high-level representations from unlabeled data. However, the high diversities in ground objects make it difficult to learn the featu
Externí odkaz:
https://doaj.org/article/875f43783c1049fa9564550234a12d35
Autor:
Van-Thuan Tran, Wei-Ho Tsai
Publikováno v:
IEEE Access, Vol 12, Pp 135651-135666 (2024)
In a recent study, we have uncovered the potential of utilizing impact sounds generated when objects fall freely and strike a plane for acoustic-based object recognition (AOR). Building upon this discovery, we address the practical scenario where mul
Externí odkaz:
https://doaj.org/article/4659feccedaa4b2c83ae5a6ee8943994
Autor:
Aminollah Khormali, Jiann-Shiun Yuan
Publikováno v:
IEEE Access, Vol 12, Pp 58114-58127 (2024)
Deepfake detection methods have shown promising results in recognizing forgeries within a given dataset, where training and testing take place on the in-distribution dataset. However, their performance deteriorates significantly when presented with u
Externí odkaz:
https://doaj.org/article/c4497c31ee284f70a112879294805db6