Search Results for author: Hadar Averbuch-Elor

Found 17 papers, 10 papers with code

Who's Waldo? Linking People Across Text and Images

1 code implementation ICCV 2021 Claire Yuqing Cui, Apoorv Khandelwal, Yoav Artzi, Noah Snavely, Hadar Averbuch-Elor

We present a task and benchmark dataset for person-centric visual grounding, the problem of linking between people named in a caption and people pictured in an image.

 Ranked #1 on Person-centric Visual Grounding on Who’s Waldo (using extra training data)

Person-centric Visual Grounding

Towers of Babel: Combining Images, Language, and 3D Geometry for Learning Multimodal Vision

1 code implementation ICCV 2021 Xiaoshi Wu, Hadar Averbuch-Elor, Jin Sun, Noah Snavely

The abundance and richness of Internet photos of landmarks and cities has led to significant progress in 3D vision over the past two decades, including automated 3D reconstructions of the world's landmarks from tourist photos.

Image Captioning

Extreme Rotation Estimation using Dense Correlation Volumes

1 code implementation CVPR 2021 Ruojin Cai, Bharath Hariharan, Noah Snavely, Hadar Averbuch-Elor

We present a technique for estimating the relative 3D rotation of an RGB image pair in an extreme setting, where the images have little or no overlap.

Learning Multimodal Affinities for Textual Editing in Images

no code implementations18 Mar 2021 Or Perel, Oron Anschel, Omri Ben-Eliezer, Shai Mazor, Hadar Averbuch-Elor

Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent.

An Ethical Highlighter for People-Centric Dataset Creation

no code implementations27 Nov 2020 Margot Hanley, Apoorv Khandelwal, Hadar Averbuch-Elor, Noah Snavely, Helen Nissenbaum

Important ethical concerns arising from computer vision datasets of people have been receiving significant attention, and a number of datasets have been withdrawn as a result.

Hidden Footprints: Learning Contextual Walkability from 3D Human Trails

no code implementations ECCV 2020 Jin Sun, Hadar Averbuch-Elor, Qianqian Wang, Noah Snavely

Predicting where people can walk in a scene is important for many tasks, including autonomous driving systems and human behavior analysis.

Autonomous Driving

Co-occurrence Based Texture Synthesis

1 code implementation17 May 2020 Anna Darzi, Itai Lang, Ashutosh Taklikar, Hadar Averbuch-Elor, Shai Avidan

As image generation techniques mature, there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.

Image Generation Texture Classification +1

ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation

3 code implementations CVPR 2020 Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, Roee Litman

This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.

Domain Adaptation Handwriting Recognition +2

READ: Recursive Autoencoders for Document Layout Generation

no code implementations1 Sep 2019 Akshay Gadi Patil, Omri Ben-Eliezer, Or Perel, Hadar Averbuch-Elor

Creating large varieties of plausible document layouts can be a tedious task, requiring numerous constraints to be satisfied, including local ones relating different semantic elements and global constraints on the general appearance and spacing.

Implicit Pairs for Boosting Unpaired Image-to-Image Translation

no code implementations15 Apr 2019 Yiftach Ginger, Dov Danon, Hadar Averbuch-Elor, Daniel Cohen-Or

As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets.

Image-to-Image Translation Translation

Clustering-driven Deep Embedding with Pairwise Constraints

1 code implementation22 Mar 2018 Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger, Daniel Cohen-Or

In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.

Co-segmentation for Space-Time Co-located Collections

no code implementations31 Jan 2017 Hadar Averbuch-Elor, Johannes Kopf, Tamir Hazan, Daniel Cohen-Or

Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image.

Border-Peeling Clustering

1 code implementation14 Dec 2016 Hadar Averbuch-Elor, Nadav Bar, Daniel Cohen-Or

In this paper, we present a novel non-parametric clustering technique.

Spherical Embedding of Inlier Silhouette Dissimilarities

no code implementations CVPR 2015 Etai Littwin, Hadar Averbuch-Elor, Daniel Cohen-Or

In this paper, we introduce a spherical embedding technique to position a given set of silhouettes of an object as observed from a set of cameras arbitrarily positioned around the object.

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