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About

About

Luís Vilaça is a PhD student in Computer Engineering at the Faculty of Engineering of the University of Porto (FEUP) and an assistant researcher at Institute for Systems and Computer Engineering, Technology and Science (INESC TEC). In addition, he is also an invited teaching assistant at the School of Engineering from the Polytechnic of Porto (ISEP).

He conducts his research at the Multimedia and Communications Technologies (MCT) group within the Centre for Telecommunications and Multimedia (CTM) and his research interests include computer vision and multimodal machine learning for multimedia content recommendation/search. Luís's PhD, “Semantic-aware Audio-Visual Representations for Multimedia Assets”, focuses on the development of deep representation models that capture the structure of videos (storytelling). Thus, allowing them to be indexed and searched on a timecode basis by segmenting the parts of interest within videos.

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Details

Details

  • Name

    Luís Miguel Salgado
  • Role

    Research Assistant
  • Since

    01st October 2018
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.

2021

Emotion Identification in Movies through Facial Expression Recognition

Authors
Almeida, J; Vilaca, L; Teixeira, IN; Viana, P;

Publication
APPLIED SCIENCES-BASEL

Abstract
Understanding how acting bridges the emotional bond between spectators and films is essential to depict how humans interact with this rapidly growing digital medium. In recent decades, the research community made promising progress in developing facial expression recognition (FER) methods. However, no emphasis has been put in cinematographic content, which is complex by nature due to the visual techniques used to convey the desired emotions. Our work represents a step towards emotion identification in cinema through facial expressions' analysis. We presented a comprehensive overview of the most relevant datasets used for FER, highlighting problems caused by their heterogeneity and to the inexistence of a universal model of emotions. Built upon this understanding, we evaluated these datasets with a standard image classification models to analyze the feasibility of using facial expressions to determine the emotional charge of a film. To cope with the problem of lack of datasets for the scope under analysis, we demonstrated the feasibility of using a generic dataset for the training process and propose a new way to look at emotions by creating clusters of emotions based on the evidence obtained in the experiments.