no code implementations • 28 Jun 2022 • Franck Dary, Maxime Petit, Alexis Nasr
Greedy algorithms for NLP such as transition based parsing are prone to error propagation.
no code implementations • 30 Jul 2020 • Maxime Petit, Emmanuel Dellandrea, Liming Chen
In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects.
1 code implementation • 10 Jul 2019 • Thomas Duboudin, Maxime Petit, Liming Chen
We propose a procedural fruit tree rendering framework, based on Blender and Python scripts allowing to generate quickly labeled dataset (i. e. including ground truth semantic segmentation).
no code implementations • 26 Sep 2018 • Maxime Petit, Amaury Depierre, Xiaofang Wang, Emmanuel Dellandréa, Liming Chen
In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i. e. learning from scratch all the time).
1 code implementation • 12 Jun 2017 • Clément Moulin-Frier, Tobias Fischer, Maxime Petit, Grégoire Pointeau, Jordi-Ysard Puigbo, Ugo Pattacini, Sock Ching Low, Daniel Camilleri, Phuong Nguyen, Matej Hoffmann, Hyung Jin Chang, Martina Zambelli, Anne-Laure Mealier, Andreas Damianou, Giorgio Metta, Tony J. Prescott, Yiannis Demiris, Peter Ford Dominey, Paul F. M. J. Verschure
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot.
no code implementations • CVPR 2016 • Hyung Jin Chang, Tobias Fischer, Maxime Petit, Martina Zambelli, Yiannis Demiris
In this paper, we present a novel framework for finding the kinematic structure correspondence between two objects in videos via hypergraph matching.