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20182021
most citedDrop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers

26 citations · 26 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV202126 cited

Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers

Nikita Dvornik, Isma Hadji, Konstantinos G. Derpanis +2

In this work, we consider the problem of sequence-to-sequence alignment for signals containing outliers. Assuming the absence of outliers, the standard Dynamic Time Warping (DTW) a…

cs.CV2021

Representation Learning via Global Temporal Alignment and Cycle-Consistency

Isma Hadji, Konstantinos G. Derpanis, Allan D. Jepson

We introduce a weakly supervised method for representation learning based on aligning temporal sequences (e.g., videos) of the same process (e.g., human action). The main idea is t…

cs.CV2020

Cycle-Consistent Generative Rendering for 2D-3D Modality Translation

Tristan Aumentado-Armstrong, Alex Levinshtein, Stavros Tsogkas +2

For humans, visual understanding is inherently generative: given a 3D shape, we can postulate how it would look in the world; given a 2D image, we can infer the 3D structure that l…

eess.IV2020

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

Kai Zhang, Martin Danelljan, Yawei Li +75

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…

cs.CV2019

Geometric Disentanglement for Generative Latent Shape Models

Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson +1

Representing 3D shape is a fundamental problem in artificial intelligence, which has numerous applications within computer vision and graphics. One avenue that has recently begun t…

cs.CV2018

Scene Categorization from Contours: Medial Axis Based Salience Measures

Morteza Rezanejad, Gabriel Downs, John Wilder +4

The computer vision community has witnessed recent advances in scene categorization from images, with the state-of-the art systems now achieving impressive recognition rates on cha…