模范
发表于 2025-3-27 00:10:46
Near-Infrared Image Colorization Using Unsupervised Contrastive Learning,e shown approximately 57% improvement in terms of Fréchet inception distance (FID) with reduced training time and less memory usage. Finally, a thorough comparative study based on different datasets is carried out to confirm superiority over leading colorization approaches in qualitative and quantitative assessments.
Mindfulness
发表于 2025-3-27 02:08:20
,Cross View and Cross Walking Gait Recognition Using a Convolutional Neural Network,tion. The learned gait features from CNN are then fed into a K-NN classifier to identify individuals based on their unique gait patterns. Experiments are carried out for cross-view and cross-walking gait recognition using the CASIA-B dataset. Our experimental results demonstrate the effectiveness of the proposed method.
闲荡
发表于 2025-3-27 08:58:16
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SSRIS
发表于 2025-3-27 10:31:00
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表示向前
发表于 2025-3-27 17:05:23
Agarwal-Cooley Convolution Algorithm,in the network. In essence, this study intends to improve the way medical images are shared across various stakeholders in a healthcare ecosystem and aims to provide a safe and secure model for the same using a combination of blockchain and image steganography.
depreciate
发表于 2025-3-27 20:20:55
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Melanocytes
发表于 2025-3-27 22:30:42
Unilateral quadratic matrix equationsltrasound volume reconstruction and demonstrate its efficacy with an . tube phantom study and an . bone experiment. The comparison between a sensorless freehand and the proposed mechanical track based acquisition is available online (.).
门闩
发表于 2025-3-28 05:12:59
CoreDeep: Improving Crack Detection Algorithms Using Width Stochasticity, approach improves detectability and significantly reduces false positives and negatives. We have measured the performance of our algorithm objectively in terms of mean intersection over union (mIoU) and subjectively in terms of perceptual quality.
改变立场
发表于 2025-3-28 09:48:56
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招募
发表于 2025-3-28 14:06:15
,Is Grad-CAM Explainable in Medical Images?,ications of Grad-CAM. The findings highlight the potential of Explainable Deep Learning and Grad-CAM in improving the accuracy and interpretability of deep learning models in medical imaging. The code is available in (.).