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About

Maria Teresa Andrade is an Assistant Professor at FEUP, at DEEC. She obtained a degree in Electrotechnical and Computing Engineering in 1986, the MSc in 1992 and the PhD in 2008, at FEUP. She participates in research activities at INESC TEC, integrated in the research team of the Multimedia Systems Area of the Center for Telecommunications and Multimedia. Main interests include context-awareness, mobile and adaptable multimedia applications in heterogeneous environments; 3D and multiview video streaming; quality of service and of experience in multimedia services; semantic technologies and content recommendation; digital television, digital cinema and new media.

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Topics
Details

Details

011
Publications

2022

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

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

Publication
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

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

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

Publication
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

2021

A Systematic Survey of ML Datasets for Prime CV Research Areas—Media and Metadata

Authors
Castro, HF; Cardoso, JS; Andrade, MT;

Publication
DATA

Abstract
The ever-growing capabilities of computers have enabled pursuing Computer Vision through Machine Learning (i.e., MLCV). ML tools require large amounts of information to learn from (ML datasets). These are costly to produce but have received reduced attention regarding standardization. This prevents the cooperative production and exploitation of these resources, impedes countless synergies, and hinders ML research. No global view exists of the MLCV dataset tissue. Acquiring it is fundamental to enable standardization. We provide an extensive survey of the evolution and current state of MLCV datasets (1994 to 2019) for a set of specific CV areas as well as a quantitative and qualitative analysis of the results. Data were gathered from online scientific databases (e.g., Google Scholar, CiteSeerX). We reveal the heterogeneous plethora that comprises the MLCV dataset tissue; their continuous growth in volume and complexity; the specificities of the evolution of their media and metadata components regarding a range of aspects; and that MLCV progress requires the construction of a global standardized (structuring, manipulating, and sharing) MLCV “library”. Accordingly, we formulate a novel interpretation of this dataset collective as a global tissue of synthetic cognitive visual memories and define the immediately necessary steps to advance its standardization and integration.

2021

SmoothMV

Authors
da Costa, TS; Andrade, MT; Viana, P;

Publication
PROCEEDINGS OF THE 2021 INTERNATIONAL WORKSHOP ON IMMERSIVE MIXED AND VIRTUAL ENVIRONMENT SYSTEMS (MMVE '21)

Abstract

2021

When Problems Only Get Bigger: The Impact of Adverse Childhood Experience on Adult Health

Authors
Novais, M; Henriques, T; Vidal Alves, MJ; Magalhaes, T;

Publication
FRONTIERS IN PSYCHOLOGY

Abstract
Introduction: Previous studies have shown that adverse childhood experiences negatively impact child development, with consequences throughout the lifespan. Some of these consequences include the exacerbation or onset of several pathologies and risk behaviors. Materials and Methods: A convenience sample of 398 individuals aged 20 years or older from the Porto metropolitan area, with quotas, was collected. The evaluation was conducted using an anonymous questionnaire that included sociodemographic questions about exposure to adverse childhood experiences, a list of current health conditions, questions about risk behaviors, the AUDIT-C test, the Fagerstrom test and the Childhood Trauma Questionnaire-brief form. Variables were quantified to measure adverse childhood experiences, pathologies, and risk behaviors in adult individuals for comparison purposes. Results: Individuals with different forms of adverse childhood experiences present higher rates of smoking dependence, self-harm behaviors, victimization of/aggression toward intimate partners, early onset of sexual life, sexually transmitted infections, multiple sexual partners, abortions, anxiety, depression, diabetes, arthritis, high cholesterol, hypertension, and stroke. Different associations are analyzed and presented. Discussion and Conclusions: The results show that individuals with adverse childhood experiences have higher total scores for more risk behaviors and health conditions than individuals without traumatic backgrounds. These results are relevant for health purposes and indicate the need for further research to promote preventive and protective measures.

Supervised
thesis

2021

Predicting IUCN conservation status through machine learning: a case study on reptiles

Author
Nádia Filipa de Jesus Soares

Institution
UP-FCUP

2021

Transforming the physical small and medium enterprise into a remote organization

Author
Célia Catarino Oliva Saraiva

Institution
UTAD

2021

The Role of Business and Innovation Strategies in Company Competitive Performance in an Uncertain Context

Author
Sofia Pinto Simões Figueira Canada

Institution
UP-FEP

2021

Hyperspectral data analysis for agriculture applications

Author
Jonás Hruska

Institution
UTAD

2021

Weather VIS

Author
Diogo Henrique de Almeida Silva Pereira

Institution
UP-FEUP