展览
发表于 2025-3-25 07:22:00
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Host142
发表于 2025-3-25 08:56:02
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SHRIK
发表于 2025-3-25 15:19:37
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AVERT
发表于 2025-3-25 16:09:16
https://doi.org/10.1007/978-981-16-1692-1iments on VOC2007 suggest that a modest extra time is needed to obtain per-class object counts compared to labeling only object categories in an image. Furthermore, we reduce the annotation time by more than 2. and 38. compared to center-click and bounding-box annotations.
CRUDE
发表于 2025-3-25 22:09:44
The Separation of Bahrain from Iran,method using an attention model. In the experiment, we show DeepVQA remarkably achieves the state-of-the-art prediction accuracy of more than 0.9 correlation, which is .5% higher than those of conventional methods on the LIVE and CSIQ video databases.
colloquial
发表于 2025-3-26 02:33:26
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Admire
发表于 2025-3-26 05:29:10
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连接
发表于 2025-3-26 10:49:02
Fictitious GAN: Training GANs with Historical Modelsious GAN can effectively resolve some convergence issues that cannot be resolved by the standard training approach. It is proved that asymptotically the average of the generator outputs has the same distribution as the data samples.
Mindfulness
发表于 2025-3-26 16:32:33
C-WSL: Count-Guided Weakly Supervised Localizationiments on VOC2007 suggest that a modest extra time is needed to obtain per-class object counts compared to labeling only object categories in an image. Furthermore, we reduce the annotation time by more than 2. and 38. compared to center-click and bounding-box annotations.
有花
发表于 2025-3-26 19:43:16
Deep Video Quality Assessor: From Spatio-Temporal Visual Sensitivity to a Convolutional Neural Aggremethod using an attention model. In the experiment, we show DeepVQA remarkably achieves the state-of-the-art prediction accuracy of more than 0.9 correlation, which is .5% higher than those of conventional methods on the LIVE and CSIQ video databases.