附录
发表于 2025-3-23 12:08:36
Learning from PhotoShop Operation Videos: The PSOV Datasetorresponding evaluation metrics. To demonstrate that the PSOV dataset has sufficient data and labeling for data-driven methods, we develop a deep learning based algorithm for the command classification task. We also carry out experiments and analysis with the proposed method to encourage better unde
壁画
发表于 2025-3-23 14:11:59
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天然热喷泉
发表于 2025-3-23 18:22:39
Symmetry-Aware Face Completion with Generative Adversarial Networksur method is capable of synthesizing semantically valid and visually plausible contents for the missing facial key parts from random mask. In addition, our model outperforms other methods for detail completion of facial components.
高贵领导
发表于 2025-3-23 22:48:36
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babble
发表于 2025-3-24 03:37:03
Fusing Solar and Stellar Cosmologies,el that predicts a set of grasp hypotheses in under 60 ms, which is critical for real-time robotic applications. The grasp detection accuracy reaches over . for unseen objects, outperforming the current state of the art on this task.
抚育
发表于 2025-3-24 07:43:55
The Wider Copernican Revolution,rmation and handle the domain distribution mismatch simultaneously. Our key novelties are: (1) a global visual-depth metric construction algorithm that can effectively align RGB and depth data structure; (2) adaptive transformed component extraction for target domain that conditioned on invariant tr
Outmoded
发表于 2025-3-24 12:49:11
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臭名昭著
发表于 2025-3-24 17:55:21
https://doi.org/10.1007/978-981-13-8699-2ating cost-intensive operations in Softmax (. exponential and division) with cost-effective operations (. addition and bit shifts). We designed and synthesized a hardware unit for our approximation approach, to estimate the area and energy consumption. In addition, we introduce a training method to
obnoxious
发表于 2025-3-24 22:38:45
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opprobrious
发表于 2025-3-25 00:17:20
Farmers, Fossils, and Heat, July 18–22e of subspace-based classifiers such as sparse representation-based classification. We describe how the structured loss function of NSFE can be optimized in a batch-by-batch fashion by a two-step alternating algorithm. The algorithm makes very few assumptions about the form of the embedding to be le