26 citations · 52 across the 11 of their papers we have counts for
15 papers
Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradients
Mete Kemertas, Allan D. Jepson, Amir-massoud Farahmand
We propose Mirror Descent Optimal Transport (MDOT), a novel method for solving discrete optimal transport (OT) problems with high precision, by unifying temperature annealing in en…
StepFormer: Self-supervised Step Discovery and Localization in Instructional Videos
Nikita Dvornik, Isma Hadji, Ran Zhang +4
Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, wi…
Efficient Flow-Guided Multi-frame De-fencing
Stavros Tsogkas, Fengjia Zhang, Allan Jepson +1
Taking photographs ''in-the-wild'' is often hindered by fence obstructions that stand between the camera user and the scene of interest, and which are hard or impossible to avoid.…
P3IV: Probabilistic Procedure Planning from Instructional Videos with Weak Supervision
He Zhao, Isma Hadji, Nikita Dvornik +3
In this paper, we study the problem of procedure planning in instructional videos. Here, an agent must produce a plausible sequence of actions that can transform the environment fr…
Uncertainty-based Cross-Modal Retrieval with Probabilistic Representations
Leila Pishdad, Ran Zhang, Konstantinos G. Derpanis +2
Probabilistic embeddings have proven useful for capturing polysemous word meanings, as well as ambiguity in image matching. In this paper, we study the advantages of probabilistic…
Approximate Policy Iteration with Bisimulation Metrics
Mete Kemertas, Allan Jepson
Bisimulation metrics define a distance measure between states of a Markov decision process (MDP) based on a comparison of reward sequences. Due to this property they provide theore…
Representing 3D Shapes with Probabilistic Directed Distance Fields
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
Differentiable rendering is an essential operation in modern vision, allowing inverse graphics approaches to 3D understanding to be utilized in modern machine learning frameworks.…
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
Vineeth S. Bhaskara, Tristan Aumentado-Armstrong, Allan Jepson +1
Modern generative adversarial networks (GANs) predominantly use piecewise linear activation functions in discriminators (or critics), including ReLU and LeakyReLU. Such models lear…
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…
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…
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models
Tristan Aumentado-Armstrong, Stavros Tsogkas, Sven Dickinson +1
A complete representation of 3D objects requires characterizing the space of deformations in an interpretable manner, from articulations of a single instance to changes in shape ac…
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…
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…
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…
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…