Search Results for author: Vadim Tikhanoff

Found 5 papers, 3 papers with code

From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach

no code implementations28 Dec 2020 Elisa Maiettini, Andrea Maracani, Raffaello Camoriano, Giulia Pasquale, Vadim Tikhanoff, Lorenzo Rosasco, Lorenzo Natale

We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning).

Active Learning Line Detection +4

Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language

no code implementations9 Jul 2019 Jennifer J. Gago, Valentina Vasco, Bartek Łukawski, Ugo Pattacini, Vadim Tikhanoff, Juan G. Victores, Carlos Balaguer

Natural language to sign language translation presents several challenges to developers, such as the discordance between the length of input and output data and the use of non-manual markers.

Sign Language Translation Translation

Face Landmark-based Speaker-Independent Audio-Visual Speech Enhancement in Multi-Talker Environments

1 code implementation6 Nov 2018 Giovanni Morrone, Luca Pasa, Vadim Tikhanoff, Sonia Bergamaschi, Luciano Fadiga, Leonardo Badino

In this paper, we address the problem of enhancing the speech of a speaker of interest in a cocktail party scenario when visual information of the speaker of interest is available.

Speech Enhancement Speech Separation

Markerless visual servoing on unknown objects for humanoid robot platforms

1 code implementation12 Oct 2017 Claudio Fantacci, Giulia Vezzani, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale

To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape.

Robotics Systems and Control Computation

Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms

1 code implementation14 Mar 2017 Claudio Fantacci, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale

This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras.

Robotics

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