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Eric Granger

LIVIA

10 papers hereh-index 324.2k citations173 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author7
  • last author3

Across the 10 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.LG4
affiliations
  • LIVIA
  • Dept. of Systems Engineering
  • Ecole de technologie superieure, Montreal
Homepage
same name
  • Eric Granger — 35 papers, h 32
  • Eric Granger — 10 papers, h 3
  • Eric Granger — 8 papers, h 4
  • Eric Granger — 1 paper, h 1
  • Eric Granger — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172019
most citedMultimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition

43 citations · 66 across the 4 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.LG2018

Scalable Laplacian K-modes

Imtiaz Masud Ziko, Eric Granger, Ismail Ben Ayed

We advocate Laplacian K-modes for joint clustering and density mode finding, and propose a concave-convex relaxation of the problem, which yields a parallel algorithm that scales u…

cs.CV2018

Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses

Jérôme Rony, Luiz G. Hafemann, Luiz S. Oliveira +3

Research on adversarial examples in computer vision tasks has shown that small, often imperceptible changes to an image can induce misclassification, which has security implication…

cs.CV2018

Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks

Jose Dolz, Xiaopan Xu, Jerome Rony +7

Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision…

cs.CV2018

Constrained-CNN losses for weakly supervised segmentation

Hoel Kervadec, Jose Dolz, Meng Tang +3

Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need fo…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.