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pro vyhledávání: '"Lu, Aidong"'
Recently, transformers have demonstrated great potential for modeling long-term dependencies from skeleton sequences and thereby gained ever-increasing attention in skeleton action recognition. However, the existing transformer-based approaches heavi
Externí odkaz:
http://arxiv.org/abs/2407.12322
Skeleton-based motion visualization is a rising field in computer vision, especially in the case of virtual reality (VR). With further advancements in human-pose estimation and skeleton extracting sensors, more and more applications that utilize skel
Externí odkaz:
http://arxiv.org/abs/2405.05428
The understanding of visual analytics process can benefit visualization researchers from multiple aspects, including improving visual designs and developing advanced interaction functions. However, the log files of user behaviors are still hard to an
Externí odkaz:
http://arxiv.org/abs/2311.00690
In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the semantic distinction of human action features is often represented by local
Externí odkaz:
http://arxiv.org/abs/2305.00666
Fully supervised skeleton-based action recognition has achieved great progress with the blooming of deep learning techniques. However, these methods require sufficient labeled data which is not easy to obtain. In contrast, self-supervised skeleton-ba
Externí odkaz:
http://arxiv.org/abs/2209.02399
Existing deep learning-based human mesh reconstruction approaches have a tendency to build larger networks in order to achieve higher accuracy. Computational complexity and model size are often neglected, despite being key characteristics for practic
Externí odkaz:
http://arxiv.org/abs/2111.12696
Both fair machine learning and adversarial learning have been extensively studied. However, attacking fair machine learning models has received less attention. In this paper, we present a framework that seeks to effectively generate poisoning samples
Externí odkaz:
http://arxiv.org/abs/2110.08932
Autor:
Shang, Qianwen, Xue, Lian, Lu, Aidong, Jia, Yueping, Zuo, YingXi, Zeng, Huimin, Zhang, Leping
Publikováno v:
In Clinical Lymphoma, Myeloma and Leukemia June 2024 24(6):392-399
Akademický článek
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Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detect
Externí odkaz:
http://arxiv.org/abs/1803.01798