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- Université de MontréalCA17 papers
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- École de Technologie SupérieureCA6 papers
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12 papers · 1 filter
Finding and Visualizing Weaknesses of Deep Reinforcement Learning Agents
Christian Rupprecht, Cyril Ibrahim, Christopher J. Pal
As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe the learned agents. Understanding the dec…
Joint segmentation and classification of retinal arteries/veins from fundus images
Fantin Girard, Conrad Kavalec, Farida Cheriet
Objective Automatic artery/vein (A/V) segmentation from fundus images is required to track blood vessel changes occurring with many pathologies including retinopathy and cardiovasc…
Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks
Daniel Berio, Memo Akten, Frederic Fol Leymarie +2
We propose a computational framework to learn stylisation patterns from example drawings or writings, and then generate new trajectories that possess similar stylistic qualities. W…
The "something something" video database for learning and evaluating visual common sense
Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski +11
Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply abou…
Memory Efficient Multi-Scale Line Detector Architecture for Retinal Blood Vessel Segmentation
Hamza Bendaoudi, Farida Cheriet, J. M. Pierre Langlois
This paper presents a memory efficient architecture that implements the Multi-Scale Line Detector (MSLD) algorithm for real-time retinal blood vessel detection in fundus images on…
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
David Vázquez, Jorge Bernal, F. Javier Sánchez +5
Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for…