Video object linguistic grounding

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/172234

dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. GPI - Grup de Processament d'Imatge i Vídeo
dc.contributor.author	Herrera-Palacio, Alba
dc.contributor.author	Ventura, Carles
dc.contributor.author	Giró Nieto, Xavier
dc.date	2019
dc.identifier.citation	Herrera-Palacio, A.; Ventura, C.; Giro, X. Video object linguistic grounding. A: International Workshop on Multimodal Understanding and Learning for Embodied Applications. "MULEA '19 1st International Workshop on Multimodal Understanding and Learning for Embodied Applications Nice, France: October 25-25, 2019". New York: Association for Computing Machinery (ACM), 2019, p. 49-51.
dc.identifier.citation	978-1-4503-6918-3
dc.identifier.citation	https://imatge.upc.edu/web/publications/video-object-linguistic-grounding
dc.identifier.citation	https://doi.org/10.1145/3347450.3357662
dc.identifier.uri	http://hdl.handle.net/2117/172234
dc.language.iso	eng
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	https://dl.acm.org/citation.cfm?id=3357662
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject	Neural networks (Computer science)
dc.subject	Linguistics
dc.subject	Image processing -- Digital techniques
dc.subject	Video object gounding
dc.subject	Neural networks
dc.subject	Linguistics
dc.subject	Xarxes neuronals (Informàtica) -- Aplicacions
dc.subject	Lingüística
dc.subject	Imatges -- Processament -- Tècniques digitals
dc.title	Video object linguistic grounding
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	The goal of this work is segmenting on a video sequence the objects which are mentioned in a linguistic description of the scene. We have adapted an existing deep neural network that achieves state of the art performance in semi-supervised video object segmentation, to add a linguistic branch that would generate an attention map over the video frames, making the segmentation of the objects temporally consistent along the sequence.
dc.description.abstract	Peer Reviewed

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