YouTube Timed Metadata Enrichment Using a Collaborative Approach

Pinto, José; Viana, Paula

doi:10.1007/978-3-319-98678-4_15

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…As a side-by-side result, this work offers an intuitive Web-based tool to annotate on a regional basis large image datasets, automatically and/or in an assisted manner, following former results on crowdsourced metadata [ 20 , 21 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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“…In previous work, we have exploited the use of semantic dictionaries that enable extracting semantically related concepts ( Viana & Pinto, 2017 ) to enhance public-contributed metadata ( Pinto & Viana, 2013 ; Pinto & Viana, 2015 ). Additionally, a methodology to improve YouTube content descriptions, by using this metadata, was proposed ( Pinto & Viana, 2018 ). The work presented in this paper exploits Natural Language Processing (NLP) and neural networks to improve existing solutions in annotation tasks, but it can also be used for other purposes, which will be described in more detail in this document.…”

Section: Introductionmentioning

confidence: 99%

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

Pinto

Viana

Teixeira

et al. 2022

PeerJ Computer Science

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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.

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Snippet Generation Using Local Alignment for Information Retrieval (LAIR)

Yilmaz,

Cicekli

2024

Engineering Cyber-Physical Systems and Critical Infrastructures

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YouTube Timed Metadata Enrichment Using a Collaborative Approach

Cited by 5 publications

References 9 publications

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

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

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

Snippet Generation Using Local Alignment for Information Retrieval (LAIR)

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