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Titlebook: Image and Vision Computing; 37th International C Wei Qi Yan,Minh Nguyen,Martin Stommel Conference proceedings 2023 The Editor(s) (if applic

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樓主: 復(fù)雜
31#
發(fā)表于 2025-3-26 21:23:32 | 只看該作者
,Extending Temporal Data Augmentation for?Video Action Recognition,ugmentation for videos, however, still remains an under-explored research topic, as most works have been treating inputs as stacks of static images rather than temporally linked series of data. Recently, it has been shown that involving the time dimension when designing augmentations can be superior
32#
發(fā)表于 2025-3-27 02:35:42 | 只看該作者
33#
發(fā)表于 2025-3-27 06:41:05 | 只看該作者
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發(fā)表于 2025-3-27 11:48:17 | 只看該作者
35#
發(fā)表于 2025-3-27 15:03:36 | 只看該作者
36#
發(fā)表于 2025-3-27 20:33:07 | 只看該作者
,Assessing the?Condition of?Copper Conductors Using Deep Learning,lly inspecting the conductors is a non-destructive testing method that can quickly identify sign of damage and degradation on the conductor’s surface and the result is used to inform decision in the risk management framework. In this study, a deep neural network is employed to classify a conductor’s
37#
發(fā)表于 2025-3-27 23:47:47 | 只看該作者
38#
發(fā)表于 2025-3-28 02:48:08 | 只看該作者
39#
發(fā)表于 2025-3-28 09:13:13 | 只看該作者
Explainable Network Pruning for Model Acceleration Based on Filter Similarity and Importance,amaging performance, particularly in computer vision applications. Most filter-level network pruning algorithms focus on minimizing the impact of pruning on network performance using either importance-based or similarity-based pruning approaches. However, no study has attempted to compare the effect
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發(fā)表于 2025-3-28 12:30:36 | 只看該作者
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