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Titlebook: Computer Vision – ACCV 2018; 14th Asian Conferenc C. V. Jawahar,Hongdong Li,Konrad Schindler Conference proceedings 2019 Springer Nature Sw

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樓主: Colossal
31#
發(fā)表于 2025-3-27 00:58:37 | 只看該作者
32#
發(fā)表于 2025-3-27 01:15:41 | 只看該作者
33#
發(fā)表于 2025-3-27 07:47:26 | 只看該作者
Knowledge Distillation with Feature Maps for Image Classificationtudent networks have less than 1% accuracy loss comparing to their teacher models for CIFAR-100 datasets. The student networks are 2–6 times faster than their teacher models for inference, and the model size of MobileNet is less than half of DenseNet-100’s.
34#
發(fā)表于 2025-3-27 10:20:47 | 只看該作者
A Temporally-Aware Interpolation Network for Video Frame Inpainting experiments demonstrate that our approach produces more accurate and qualitatively satisfying results than a state-of-the-art video prediction method and many strong frame inpainting baselines. Our code is available at ..
35#
發(fā)表于 2025-3-27 15:08:42 | 只看該作者
Linear Solution to the Minimal Absolute Pose Rolling Shutter Probleminimal number of 9 correspondences but provides even better results than the state-of-the-art R6P. Moreover, all proposed linear solvers provide a single solution while the state-of-the-art R6P provides up?to 20 solutions which have to be pruned by expensive verification.
36#
發(fā)表于 2025-3-27 21:19:31 | 只看該作者
37#
發(fā)表于 2025-3-27 22:21:30 | 只看該作者
38#
發(fā)表于 2025-3-28 06:02:31 | 只看該作者
Advances in Cryptology – CRYPTO 2018f RankGAN. We focus on face images from the CelebA dataset in our work and show visual as well as quantitative improvements in face generation and completion tasks over other GAN approaches, including WGAN and LSGAN.
39#
發(fā)表于 2025-3-28 07:43:29 | 只看該作者
40#
發(fā)表于 2025-3-28 11:14:58 | 只看該作者
https://doi.org/10.1007/978-3-030-20893-6artificial intelligence; computer vision; estimation; image coding; image processing; image reconstructio
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