most citedEdgeConnect: Generative Image Inpainting with Adversarial Edge Learning

589 citations · 592 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV20192 cited

Real-time Video Summarization on Commodity Hardware

Wesley Taylor, Faisal Z. Qureshi

We present a method for creating video summaries in real-time on commodity hardware. Real-time here refers to the fact that the time required for video summarization is less than t…

cs.CV2019589 cited

EdgeConnect: Generative Image Inpainting with Adversarial Edge Learning

Kamyar Nazeri, Eric Ng, Tony Joseph +2

Over the last few years, deep learning techniques have yielded significant improvements in image inpainting. However, many of these techniques fail to reconstruct reasonable struct…

cs.CV20191 cited

Joint Spatial and Layer Attention for Convolutional Networks

Tony Joseph, Konstantinos G. Derpanis, Faisal Z. Qureshi

In this paper, we propose a novel approach that learns to sequentially attend to different Convolutional Neural Networks (CNN) layers (i.e., ``what'' feature abstraction to attend…

cs.CV2018

Unsupervised Deep Features for Remote Sensing Image Matching via Discriminator Network

Mohbat Tharani, Numan Khurshid, Murtaza Taj

The advent of deep perceptual networks brought about a paradigm shift in machine vision and image perception. Image apprehension lately carried out by hand-crafted features in the…

stat.ML2018

Neural Networks Trained to Solve Differential Equations Learn General Representations

Martin Magill, Faisal Qureshi, Hendrick W. de Haan

We introduce a technique based on the singular vector canonical correlation analysis (SVCCA) for measuring the generality of neural network layers across a continuously-parametrize…

cs.CV2018

A Framework for Video-Driven Crowd Synthesis

Jordan Stadler, Faisal Z. Qureshi

We present a framework for video-driven crowd synthesis. Motion vectors extracted from input crowd video are processed to compute global motion paths. These paths encode the domina…