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pro vyhledávání: '"Qi, Xiaojun"'
With the widespread adoption of digital devices equipped with cameras and the rapid development of Internet technology, numerous content-based image retrieval systems and novel image feature extraction techniques have emerged in recent years. This pa
Externí odkaz:
http://arxiv.org/abs/2411.08567
Autor:
Chen, Qiuxiao, Qi, Xiaojun
Retrieving spatial information and understanding the semantic information of the surroundings are important for Bird's-Eye-View (BEV) semantic segmentation. In the application of autonomous driving, autonomous vehicles need to be aware of their surro
Externí odkaz:
http://arxiv.org/abs/2312.04044
Autor:
Farokhi, Soheila, Yaramala, Aswani, Huang, Jiangtao, Khan, Muhammad F. A., Qi, Xiaojun, Karimi, Hamid
In recent years, Massive Open Online Courses (MOOCs) have gained significant traction as a rapidly growing phenomenon in online learning. Unlike traditional classrooms, MOOCs offer a unique opportunity to cater to a diverse audience from different ba
Externí odkaz:
http://arxiv.org/abs/2310.12281
Publikováno v:
In Fungal Biology December 2024 128(8) Part A:2285-2294
Akademický článek
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Sparse representation has recently been successfully applied in visual tracking. It utilizes a set of templates to represent target candidates and find the best one with the minimum reconstruction error as the tracking result. In this paper, we propo
Externí odkaz:
http://arxiv.org/abs/1902.07668
Autor:
Javanmardi, Mohammadreza, Qi, Xiaojun
Sparse representation is considered as a viable solution to visual tracking. In this paper, we propose a structured group local sparse tracker (SGLST), which exploits local patches inside target candidates in the particle filter framework. Unlike the
Externí odkaz:
http://arxiv.org/abs/1902.06182
Akademický článek
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Autor:
Javanmardi, Mohammadreza, Qi, Xiaojun
Sparse representation is a viable solution to visual tracking. In this paper, we propose a structured multi-task multi-view tracking (SMTMVT) method, which exploits the sparse appearance model in the particle filter framework to track targets under d
Externí odkaz:
http://arxiv.org/abs/1806.01985
Publikováno v:
Visual Computer; Aug2024, Vol. 40 Issue 8, p5341-5356, 16p