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Title:	One shot learning for generic instance segmentation in RGBD videos
Author:	Lin, Xiao; Casas Pla, Josep Ramon; Pardàs Feliu, Montse
Other authors:	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
Abstract:	Hand-crafted features employed in classical generic instance segmentation methods have limited discriminative power to distinguish different objects in the scene, while Convolutional Neural Networks (CNNs) based semantic segmentation is restricted to predefined semantics and not aware of object instances. In this paper, we combine the advantages of the two methodologies and apply the combined approach to solve a generic instance segmentation problem in RGBD video sequences. In practice, a classical generic instance segmentation method is employed to initially detect object instances and build temporal correspondences, whereas instance models are trained based on the few detected instance samples via CNNs to generate robust features for instance segmentation. We exploit the idea of one shot learning to deal with the small training sample size problem when training CNNs. Experiment results illustrate the promising performance of the proposed approach.
Abstract:	Peer Reviewed
Subject(s):	-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic -Machine learning -Instance segmentation -One shot learning -Convolutional neural network -Aprenentatge automàtic
Rights:	Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Document type:	Article - Published version Conference Object
Published by:	Scitepress
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