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Titlebook: Computer Vision – ECCV 2016 Workshops; Amsterdam, The Nethe Gang Hua,Hervé Jégou Conference proceedings 2016 Springer International Publish

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樓主: Espionage
51#
發(fā)表于 2025-3-30 10:36:37 | 只看該作者
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發(fā)表于 2025-3-30 13:45:18 | 只看該作者
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發(fā)表于 2025-3-30 18:22:32 | 只看該作者
Vanessa di Paola,Stéphanie Moulletce recognition community. Although great progress has been made in recent years, main focus is the face recognition based on SINGLE sketch in existing studies. In this paper, we present a fundamental study of face recognition from multiple stylistic sketches. Three specific scenarios with correspond
54#
發(fā)表于 2025-3-30 21:52:23 | 只看該作者
Audrey Dumas,Philippe Méhaut,Noémie Olympiodging the sketch-photo domain gap, it also asks for instance-level discrimination within object categories. Most prior approaches focused on feature engineering and fine-grained ranking, yet neglected an important and central problem: how to establish a fine-grained cross-domain feature space to con
55#
發(fā)表于 2025-3-31 02:16:07 | 只看該作者
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發(fā)表于 2025-3-31 08:34:08 | 只看該作者
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發(fā)表于 2025-3-31 12:49:02 | 只看該作者
Clean water: a fading resource,s on generating face portrait of good quality, but ignoring the time consumption. Existing methods have large time complexity due to dense computation of patch matching in the neighbor selection process. In this paper, we propose a simple yet effective fast face sketch synthesis method based on K di
58#
發(fā)表于 2025-3-31 15:16:56 | 只看該作者
59#
發(fā)表于 2025-3-31 20:26:19 | 只看該作者
Philip K. Maini,Thomas E. Woolleyy. This is a challenging problem for uncalibrated cameras such as removable dash cams or cell phone cameras, where the location of the road in the image may vary considerably from image to image. Here we show that combining a spatial prior with vanishing point and horizon estimators can generate imp
60#
發(fā)表于 2025-3-31 21:57:04 | 只看該作者
Philip K. Maini,Thomas E. Woolley-mounted cameras from natural scene images. The proposed framework involves two contributions. First, a novel Cascaded Localization Network (CLN) joining two customized convolutional nets is proposed to detect the guide panels and the scene text on them in a coarse-to-fine manner. In this network, t
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