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Sobre

Sobre

Sou Professora Coordenadora no Politécnico do Porto e Investigadora no INESC TEC, no Centro de Telecomunicações e Multimédia, onde lidero a área de Tecnologias de Comunicação Multimédia. Tenho um Doutoramento em Engenharia Electrotécnica e de Computadores pela Universidade do Porto, com um foco na àrea da Gestão de Conteúdos Audiovisuais. Enquanto investigadora do INESC TEC, tenho sido responsável por diversos projectos Europeus e Nacionais, envolvendo parceiros da área da indústria, media e academia. Autora de diversas publicações, sou também revisora activa de artigos submetidos a conferências e revistas, membro de comissões científicas e de organização de conferências. Recentemente, organizei a série de Workshops com o tema "Immersive Media Experiences" (2013-2015) na maior conferência na área de multimédia (ACM Multimedia). Participo frequentemente como perita da Comissão Europeia ou de organismos nacionais na avaliação de propostas de investigação. Os meus interesses de investigação centram-na na área dos sistema de comunicação multimedia, incluindo televisão e novos serviços, gestão de conteúdos, personalização e recomendação, novos formatos e conteúdos imersivos e interactivos.

Tópicos
de interesse
Detalhes

Detalhes

013
Publicações

2022

Photo2Video: Semantic-Aware Deep Learning-Based Video Generation from Still Content

Autores
Viana, P; Andrade, MT; Carvalho, P; Vilaca, L; Teixeira, IN; Costa, T; Jonker, P;

Publicação
JOURNAL OF IMAGING

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

2022

Automated Adequacy Assessment of Cervical Cytology Samples Using Deep Learning

Autores
Mosiichuk, V; Viana, P; Oliveira, T; Rosado, L;

Publicação
Pattern Recognition and Image Analysis - Lecture Notes in Computer Science

Abstract

2022

Symbolic Music Generation Conditioned on Continuous-Valued Emotions

Autores
Sulun, S; Davies, MEP; Viana, P;

Publicação
IEEE ACCESS

Abstract

2022

Enhancing Photography Management Through Automatically Extracted Metadata

Autores
Carvalho, P; Freitas, D; Machado, T; Viana, P;

Publicação
INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, ISDA 2021

Abstract
The tremendous increase in photographs that are captured each day by common users has been favoured by the availability of high quality devices at accessible costs, such as smartphones and digital cameras. However, the quantity of captured photos raises new challenges regarding the access and management of image repositories. This paper describes a lightweight distributed framework intended to help overcome these problems. It uses image metadata in EXIF format, already widely added to images by digital acquisition devices, and automatic facial recognition to provide management and search functionalities. Moreover, a visualization functionality using a graph-based strategy was integrated, enabling an enhanced and more interactive navigation through search results and the corresponding relations.

2022

Improving word embeddings in Portuguese: increasing accuracy while reducing the size of the corpus

Autores
Pinto, JP; Viana, P; Teixeira, IN; Andrade, MT;

Publicação
PeerJ Comput. Sci.

Abstract
The subjectiveness of multimedia content description has a strong negative impact on tag-based information retrieval. In our work, we propose enhancing available descriptions by adding semantically related tags. To cope with this objective, we use a word embedding technique based on the Word2Vec neural network parameterized and trained using a new dataset built from online newspapers. A large number of news stories was scraped and pre-processed to build a new dataset. Our target language is Portuguese, one of the most spoken languages worldwide. The results achieved significantly outperform similar existing solutions developed in the scope of different languages, including Portuguese. Contributions include also an online application and API available for external use. Although the presented work has been designed to enhance multimedia content annotation, it can be used in several other application areas. © 2022. Pinto et al. Distributed under Creative Commons CC-BY 4.0

Teses
supervisionadas

2021

Improving quality and agility of safety-critical software development using domain-specific languages

Autor
João Ricardo Faria Mendes Almeida Reis

Instituição
UP-FEUP

2020

Automatic Emotion Identification: Analysis and Detection of Facial Expressions in Movies

Autor
João Carlos Miranda de Almeida

Instituição
UP-FEUP

2020

Implementação e análise de dados de uma rede IoT

Autor
RAFAEL NEVES MIRANDA

Instituição
IPP-ISEP

2020

Video-based music generation

Autor
Serkan Sulun

Instituição
UP-FEUP

2020

Deteção de publicidade em conteúdos de televisão sem informação a priori

Autor
Guilherme Dias Castro

Instituição
UP-FEUP