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Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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61#
發(fā)表于 2025-4-1 01:51:27 | 只看該作者
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發(fā)表于 2025-4-1 08:51:57 | 只看該作者
The Eastern Arctic Seas Encyclopediatra network parameters. We present the theoretical properties and conditions of RSC for improving cross-domain generalization. The experiments endorse the simple, effective, and architecture-agnostic nature of our RSC method.
63#
發(fā)表于 2025-4-1 11:28:57 | 只看該作者
64#
發(fā)表于 2025-4-1 17:03:07 | 只看該作者
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發(fā)表于 2025-4-1 21:46:26 | 只看該作者
https://doi.org/10.1007/978-3-319-24237-8 Notably, we allow the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN. We show that these easy-to-implement and practical changes help preserve the reconstruction to remain in the manifold of nature image, and thus l
66#
發(fā)表于 2025-4-1 23:32:06 | 只看該作者
The Eastern Arctic Seas Encyclopediasively explore the signal distribution free from the limited representation ability and inefficiency of deterministic mathematical modeling. Experimental results show that the reconstructed LFs not only achieve much higher PSNR/SSIM but also preserve the LF parallax structure better, compared with s
67#
發(fā)表于 2025-4-2 04:56:15 | 只看該作者
The Eastern Arctic Seas Encyclopediast of the temporal characteristics of the periodic physiological signals. Then we take pairwise MSTmaps as inputs to an autoencoder architecture with two encoders (one for physiological signals and the other for non-physiological information) and use a cross-verified scheme to obtain physiological f
68#
發(fā)表于 2025-4-2 07:51:10 | 只看該作者
69#
發(fā)表于 2025-4-2 12:44:40 | 只看該作者
70#
發(fā)表于 2025-4-2 16:27:36 | 只看該作者
Suppress and Balance: A Simple Gated Network for Salient Object Detection,e proposed “Fold” operation (Fold-ASPP) to accurately localize salient objects of various scales. Extensive experiments on five challenging datasets demonstrate that the proposed model performs favorably against most state-of-the-art methods under different evaluation metrics.
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