Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Jiang, Shengqin"'
Autor:
Jiang, Shengqin, Fang, Yaoyu, Zhang, Haokui, Liu, Qingshan, Qi, Yuankai, Yang, Yang, Wang, Peng
Rehearsal-based video incremental learning often employs knowledge distillation to mitigate catastrophic forgetting of previously learned data. However, this method faces two major challenges for video task: substantial computing resources from loadi
Externí odkaz:
http://arxiv.org/abs/2306.00393
Efficient crowd counting models are urgently required for the applications in scenarios with limited computing resources, such as edge computing and mobile devices. A straightforward method to achieve this is knowledge distillation (KD), which involv
Externí odkaz:
http://arxiv.org/abs/2303.10318
The counting task, which plays a fundamental role in numerous applications (e.g., crowd counting, traffic statistics), aims to predict the number of objects with various densities. Existing object counting tasks are designed for a single object class
Externí odkaz:
http://arxiv.org/abs/2212.14193
Publikováno v:
In Pattern Recognition December 2024 156
Publikováno v:
In Expert Systems With Applications 15 March 2025 265
Autor:
Tian, Wei, Yi, Lei, Niu, Xianghua, Fang, Rong, Zhang, Lixia, Liu, Huanhuan, Xu, Zhuo, Jiang, Shengqin, Zhang, Yonghong
Publikováno v:
In Neurocomputing 28 July 2024 591
Publikováno v:
In Chaos, Solitons and Fractals: the interdisciplinary journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena October 2023 175 Part 1
Publikováno v:
In ISA Transactions March 2023 134:134-143
Publikováno v:
In Neurocomputing 14 January 2023 517:257-263
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