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

Chipmunk

Antje Flüchter,Jivanta Schöttlive models. To the best of our knowledge this practice has not been studied within the context of generative deep networks. Therefore, we study domain adaptation applied to image generation with generative adversarial networks. We evaluate several aspects of domain adaptation, including the impact of

运动的我

Antje Flüchter,Jivanta Schöttliomputational saliency models are complex and inefficient. Most saliency models process high-resolution color images; however, insights into the evolutionary origins of visual salience detection suggest that achromatic low-resolution vision is essential to its speed and efficiency. Previous studies s

Intellectual

Antje Flüchter,Jivanta Schöttli, with applications ranging from modeling to robotics, through augmented reality. Nevertheless, their use is limited by their low resolution, with frames often corrupted with noise, missing data and temporal inconsistencies. Our approach, named ., consists in generating and updating through time a s

Aggressive

https://doi.org/10.1007/978-1-4615-4445-6cal tree that encodes global context information. This allows us to cope with the main limitation of random sampling in training a conventional triplet loss, which is a central issue for deep metric learning. Our main contributions are two-fold. (i) we construct a hierarchical class-level tree where

并入

Linda Clarke,Peter Gijsel,Jörn Jansseniant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semanti

Largess

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978-3-030-01230-4Springer Nature Switzerland AG 2018

Mawkish

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/234192.jpg

充满人

Computer Vision – ECCV 2018978-3-030-01231-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

Axillary

The Role of Expectations in Reasoning, using out-of-domain datasets, and stage-wise strategies for effective BusterNet training. Our extensive studies demonstrate that BusterNet outperforms state-of-the-art copy-move detection algorithms by a large margin on the two publicly available datasets, CASIA and CoMoFoD, and that it is robust against various known attacks.