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2020 Winter Conference on Applications of Computer Vision

1st - 5th March 2020
Papers. 'SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder' & 'Lightweight 3D Human Pose Estimation Network Training Using Teacher-Student Learning'

Julien Perez speaker at IDIAP, Martigny, Switzerland

4th March 2020
Julien Perez: Iterative Reasoning Path Retrieval for Multi-Hop Question Answering

EDBT/ICDT 2020 joint conference and co-located workshops

30th March - 2nd April 2020, Copenhagen, Denmark
Co-chairing ETMLP workshop at EBDT and presenting 'Culture-Aware POI Category Completion...' at DARLI AP 2020 workshop

Fast Adaptation of Deep Reinforcement Learning-Based Navigation Skills to Human Preference

Jinyoung Choi, Christopher Dance, Jung-eun Kim, Kyung-sik Park, Jaehun Han, Joonho Seo, Minsu Kim
2020 International Conference on Robotics and Automation (ICRA), 31 May - 4 June 2020, Paris, France

From Abstract Specifications to Application Generation

Jose Miguel Pérez-Álvarez, Adrian Mos
ICSE 2020 (42nd International Conference on Software Engineering) - Software Engineering in Society Track. Seoul, South Korea, 23-29 May 2020

RunAhead: Exploring Head Scanning based Navigation for Runners

Danilo Gallo, Shreepriya Shreepriya, Jutta Willamowski
CHI 2020, Honolulu, Hawaii, USA, 25 April 2020 -1 May 2020

Hybrid Wizard of Oz: Concept Testing a Recommender System

Sruthi Viswanathan, Bernard Omidvar-Tehrani, Adrien Bruyat, Frédéric Roulland, Antonietta Grasso
CHI 2020, Honolulu, USA, 25-30 April 2020
29 January 2020

Announcing Virtual KITTI 2

New release of the popular synthetic image dataset for training and testing.
13 December 2019

Towards understanding human actions out of context with the Mimetics dataset

This article introduces our recent arxiv preprint on further understanding human actions out of context thanks to the newly introduced Mimetics dataset.
6 December 2019

Explainability Matters in Machine Learning Pipelines

Workshop on methods and measures for explainability and trustworthiness of machine learning pipelines



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