squamous-cell
发表于 2025-3-21 18:53:51
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Herbivorous
发表于 2025-3-21 20:51:33
Günther Schuh,Patrick Wegehauptnce, EdgeCRF based on patches extracted from colour edges works effectively only when the presence of noise is insignificant, which is not the case for many real images; and, CRFNet, a recent method based on fully supervised deep learning works only for the CRFs that are in the training data, and he
开头
发表于 2025-3-22 01:07:13
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uncertain
发表于 2025-3-22 08:13:33
https://doi.org/10.1007/978-3-642-17032-4 work, we explore learning from abundant, randomly generated synthetic data, together with unlabeled or partially labeled target domain data, instead. Randomly generated synthetic data has the advantage of controlled variability in the lane geometry and lighting, but it is limited in terms of photo-
Cultivate
发表于 2025-3-22 09:02:41
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不遵守
发表于 2025-3-22 13:49:45
https://doi.org/10.1007/978-3-658-45553-8ims. Despite the effort of many companies requiring their own mobile applications to capture images for online transactions, it is difficult to restrict users from taking a picture of other’s images displayed on a screen. To detect such cases, we propose a novel approach using paired images with dif
不遵守
发表于 2025-3-22 19:31:33
https://doi.org/10.1007/978-3-658-45553-8t via e.g. blurring, adding noise, or graying out, which often produce unrealistic, out-of-samples. Instead, we propose to integrate a generative inpainter into three representative attribution methods to remove an input feature. Our proposed change improved all three methods in (1) generating more
诗集
发表于 2025-3-23 00:34:44
FinTech and Financial Inclusion,r sound modalities contribute to the result, i.e. do we need both image and sound for sound source localization? To address this question, we develop an unsupervised learning system that solves sound source localization by decomposing this task into two steps: (i) “potential sound source localizatio
闹剧
发表于 2025-3-23 04:38:11
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Bother
发表于 2025-3-23 07:23:49
https://doi.org/10.1007/978-3-031-24563-3nd 3D model-based methods proposed recently have their benefits and limitations. Whereas 3D model-based methods provide realistic deformations of the clothing, it needs a difficult 3D model construction process and cannot handle the non-clothing areas well. Image-based deep neural network methods ar