Search Results for author: Fabrizio Nunnari

Found 12 papers, 1 papers with code

A Software Toolkit for Pre-processing Sign Language Video Streams

1 code implementation SLTAT (LREC) 2022 Fabrizio Nunnari

We present the requirements, design guidelines, and the software architecture of an open-source toolkit dedicated to the pre-processing of sign language video material.

Proceedings of eNTERFACE 2015 Workshop on Intelligent Interfaces

no code implementations19 Jan 2018 Matei Mancas, Christian Frisson, Joëlle Tilmanne, Nicolas D'Alessandro, Petr Barborka, Furkan Bayansar, Francisco Bernard, Rebecca Fiebrink, Alexis Heloir, Edgar Hemery, Sohaib Laraba, Alexis Moinet, Fabrizio Nunnari, Thierry Ravet, Loïc Reboursière, Alvaro Sarasua, Mickaël Tits, Noé Tits, François Zajéga, Paolo Alborno, Ksenia Kolykhalova, Emma Frid, Damiano Malafronte, Lisanne Huis in't Veld, Hüseyin Cakmak, Kevin El Haddad, Nicolas Riche, Julien Leroy, Pierre Marighetto, Bekir Berker Türker, Hossein Khaki, Roberto Pulisci, Emer Gilmartin, Fasih Haider, Kübra Cengiz, Martin Sulir, Ilaria Torre, Shabbir Marzban, Ramazan Yazıcı, Furkan Burak Bâgcı, Vedat Gazi Kılı, Hilal Sezer, Sena Büsra Yenge, Charles-Alexandre Delestage, Sylvie Leleu-Merviel, Muriel Meyer-Chemenska, Daniel Schmitt, Willy Yvart, Stéphane Dupont, Ozan Can Altiok, Aysegül Bumin, Ceren Dikmen, Ivan Giangreco, Silvan Heller, Emre Külah, Gueorgui Pironkov, Luca Rossetto, Yusuf Sahillioglu, Heiko Schuldt, Omar Seddati, Yusuf Setinkaya, Metin Sezgin, Claudiu Tanase, Emre Toyan, Sean Wood, Doguhan Yeke, Françcois Rocca, Pierre-Henri De Deken, Alessandra Bandrabur, Fabien Grisard, Axel Jean-Caurant, Vincent Courboulay, Radhwan Ben Madhkour, Ambroise Moreau

The 11th Summer Workshop on Multimodal Interfaces eNTERFACE 2015 was hosted by the Numediart Institute of Creative Technologies of the University of Mons from August 10th to September 2015.

DeepHPS: End-to-end Estimation of 3D Hand Pose and Shape by Learning from Synthetic Depth

no code implementations28 Aug 2018 Jameel Malik, Ahmed Elhayek, Fabrizio Nunnari, Kiran varanasi, Kiarash Tamaddon, Alexis Heloir, Didier Stricker

Also, by employing a joint training strategy with real and synthetic data, we recover 3D hand mesh and pose from real images in 3. 7ms.

A CNN toolbox for skin cancer classification

no code implementations21 Aug 2019 Fabrizio Nunnari, Daniel Sonntag

We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification.

AutoML Classification +3

The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report

no code implementations19 May 2020 Daniel Sonntag, Fabrizio Nunnari, Hans-Jürgen Profitlich

However, the main contribution is a diagnostic and decision support system in dermatology for patients and doctors, an interactive deep learning system for differential diagnosis of malignant skin lesions.

A Competitive Deep Neural Network Approach for the ImageCLEFmed Caption 2020 Task

no code implementations11 Jul 2020 Marimuthu Kalimuthu, Fabrizio Nunnari, Daniel Sonntag

The aim of ImageCLEFmed Caption task is to develop a system that automatically labels radiology images with relevant medical concepts.

Influence of Movement Energy and Affect Priming on the Perception of Virtual Characters Extroversion and Mood

no code implementations ACM ICMI Workshop GENEA 2021 Tanja Schneeberger, Fatima Ayman Aly, Daksitha Withanage Don, Katharina Gies, Zita Zeimer, Fabrizio Nunnari, Patrick Gebhard

This paper presents different configurations of Movement Energy for virtual characters and two studies about how these influence the perception of the characters’ personality, extroversion in particular, and mood.

Fine-tuning of Convolutional Neural Networks for the Recognition of Facial Expressions in Sign Language Video Samples

no code implementations SLTAT (LREC) 2022 Neha Deshpande, Fabrizio Nunnari, Eleftherios Avramidis

In this paper, we investigate the capability of convolutional neural networks to recognize in sign language video frames the six basic Ekman facial expressions for ‘fear’, ‘disgust’, ‘surprise’, ‘sadness’, ‘happiness’, ‘anger’ along with the ‘neutral’ class.

Data Augmentation Facial Expression Recognition +1

Fine-tuning of explainable CNNs for skin lesion classification based on dermatologists' feedback towards increasing trust

no code implementations3 Apr 2023 Md Abdul Kadir, Fabrizio Nunnari, Daniel Sonntag

In this paper, we propose a CNN fine-tuning method which enables users to give simultaneous feedback on two outputs: the classification itself and the visual explanation for the classification.

Classification Lesion Classification +1

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