变化
发表于 2025-3-26 23:57:15
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GEM
发表于 2025-3-27 05:03:42
Hidden Aztecs and Absent Spaniards state-of-the-art performance of lightweight networks (such as MobileNets-V1/-V2, ShuffleNets and CondenseNet). We have tested the actual inference time on an ARM-based mobile device. The CrossNet still gets the best performance. Code and models are public available (.).
Tempor
发表于 2025-3-27 08:26:06
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太空
发表于 2025-3-27 12:27:46
Contrasting Competitive Plausibility, as far as we know. Moreover, it is able to predict pose without using any 3D information. Extensive evaluations on several challenging benchmarks such as AFLW and AFW demonstrate the effectiveness of the proposed method with competitive results.
Cupidity
发表于 2025-3-27 16:19:54
The Alhazen Optical Revolution, leads to accurate ACs. Also, the estimated homographies have similar accuracy to what the state-of-the-art methods obtain, but due to requiring only a single correspondence, the robust estimation, e.g. by Graph-Cut RANSAC, is an order of magnitude faster.
CAGE
发表于 2025-3-27 21:08:01
Pioneer Networks: Progressively Growing Generative Autoencodere network with the recently introduced adversarial encoder–generator network. The ability to reconstruct input images is crucial in many real-world applications, and allows for precise intelligent manipulation of existing images. We show promising results in image synthesis and inference, with state-of-the-art results in . inference tasks.
Decline
发表于 2025-3-27 22:46:17
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向外供接触
发表于 2025-3-28 02:45:30
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补充
发表于 2025-3-28 09:50:59
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trigger
发表于 2025-3-28 11:59:22
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