R
Roey Mechrez
Researcher at Technion – Israel Institute of Technology
Publications - 24
Citations - 1411
Roey Mechrez is an academic researcher from Technion – Israel Institute of Technology. The author has contributed to research in topics: Feature (computer vision) & Similarity (geometry). The author has an hindex of 14, co-authored 24 publications receiving 1006 citations. Previous affiliations of Roey Mechrez include Tel Aviv University.
Papers
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Book ChapterDOI
The 2018 PIRM Challenge on Perceptual Image Super-Resolution
TL;DR: This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018, and concludes with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.
Book ChapterDOI
The Contextual Loss for Image Transformation with Non-Aligned Data
TL;DR: ContextualLoss as mentioned in this paper is based on both context and semantics to compare regions with similar semantic meaning, while considering the context of the entire image, which can translate eyes-to-eyes and mouth-tomouth.
Posted Content
The Contextual Loss for Image Transformation with Non-Aligned Data
TL;DR: This work presents an alternative loss function that does not require alignment, thus providing an effective and simple solution for a new space of problems.
Proceedings ArticleDOI
Template Matching with Deformable Diversity Similarity
TL;DR: A novel measure for template matching named Deformable Diversity Similarity –, based on the diversity of feature matches between a target image window and the template, that is robust to complex deformations, significant background clutter, and occlusions is proposed.
Book ChapterDOI
Maintaining Natural Image Statistics with the Contextual Loss
TL;DR: ContextualLoss as mentioned in this paper proposes to train a feed-forward CNN to maintain natural internal statistics by explicitly looking at the distribution of features in an image and train the network to generate images with natural feature distributions.