Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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书目名称Computer Vision – ECCV 2018影响因子(影响力)




书目名称Computer Vision – ECCV 2018影响因子(影响力)学科排名




书目名称Computer Vision – ECCV 2018网络公开度




书目名称Computer Vision – ECCV 2018网络公开度学科排名




书目名称Computer Vision – ECCV 2018被引频次




书目名称Computer Vision – ECCV 2018被引频次学科排名




书目名称Computer Vision – ECCV 2018年度引用




书目名称Computer Vision – ECCV 2018年度引用学科排名




书目名称Computer Vision – ECCV 2018读者反馈




书目名称Computer Vision – ECCV 2018读者反馈学科排名

注意力集中

Programmable Triangulation Light Curtainsstead, it needs to only monitor if any object comes within its near proximity which is an easier task than full depth scanning. We introduce a novel device that monitors the presence of objects on a virtual shell near the device, which we refer to as a light curtain. Light curtains offer a light-wei

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Arboreal

Materials for Masses: SVBRDF Acquisition with a Single Mobile Phone Imageby a handheld mobile phone camera. Our method images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. We train a CNN to regress an SVBRDF and surface normals from this image. Our net

Optic-Disk

Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagatione and pose variations, the problem is compounded when instances occlude each other causing failures in tracking. In this study, we formulate a deep recurrent network that is capable of segmenting and tracking objects in video simultaneously by their temporal continuity, yet able to re-identify them

Optic-Disk

Spatio-Temporal Transformer Network for Video Restorationly consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences across several timesteps. To alleviate these problems, we propose a novel Spatio-temporal Transformer Network (STTN) which handles mult

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刺激

Multi-view to Novel View: Synthesizing Novel Views With Self-learned Confidence pose from given source images. We propose an end-to-end trainable framework that learns to exploit multiple viewpoints to synthesize a novel view without any 3D supervision. Specifically, our model consists of a flow prediction module and a pixel generation module to directly leverage information p