430 research outputs found
FrameNet annotation for multimodal corpora: devising a methodology for the semantic representation of text-image interactions in audiovisual productions
Multimodal analyses have been growing in importance within several approaches to
Cognitive Linguistics and applied fields such as Natural Language Understanding. Nonetheless
fine-grained semantic representations of multimodal objects are still lacking, especially in terms
of integrating areas such as Natural Language Processing and Computer Vision, which are key
for the implementation of multimodality in Computational Linguistics. In this dissertation, we
propose a methodology for extending FrameNet annotation to the multimodal domain, since
FrameNet can provide fine-grained semantic representations, particularly with a database
enriched by Qualia and other interframal and intraframal relations, as it is the case of FrameNet
Brasil. To make FrameNet Brasil able to conduct multimodal analysis, we outlined the
hypothesis that similarly to the way in which words in a sentence evoke frames and organize
their elements in the syntactic locality accompanying them, visual elements in video shots may,
also, evoke frames and organize their elements on the screen or work complementarily with the
frame evocation patterns of the sentences narrated simultaneously to their appearance on screen,
providing different profiling and perspective options for meaning construction. The corpus
annotated for testing the hypothesis is composed of episodes of a Brazilian TV Travel Series
critically acclaimed as an exemplar of good practices in audiovisual composition. The TV genre
chosen also configures a novel experimental setting for research on integrated image and text
comprehension, since, in this corpus, text is not a direct description of the image sequence but
correlates with it indirectly in a myriad of ways. The dissertation also reports on an eye-tracker
experiment conducted to validate the approach proposed to a text-oriented annotation. The
experiment demonstrated that it is not possible to determine that text impacts gaze directly and
was taken as a reinforcement to the approach of valorizing modes combination. Last, we present
the Frame2 dataset, the product of the annotation task carried out for the corpus following both
the methodology and guidelines proposed. The results achieved demonstrate that, at least for
this TV genre but possibly also for others, a fine-grained semantic annotation tackling the
diverse correlations that take place in a multimodal setting provides new perspective in
multimodal comprehension modeling. Moreover, multimodal annotation also enriches the
development of FrameNets, to the extent that correlations found between modalities can attest
the modeling choices made by those building frame-based resources.Análises multimodais vêm crescendo em importância em várias abordagens da
Linguística Cognitiva e em diversas áreas de aplicação, como o da Compreensão de Linguagem
Natural. No entanto, há significativa carência de representações semânticas refinadas de objetos
multimodais, especialmente em termos de integração de áreas como Processamento de
Linguagem Natural e Visão Computacional, que são fundamentais para a implementação de
multimodalidade no campo da Linguística Computacional. Nesta tese, propomos uma
metodologia para estender o método de anotação da FrameNet ao domínio multimodal, uma
vez que a FrameNet pode fornecer representações semânticas refinadas, particularmente com
um banco de dados enriquecido por Qualia e outras relações interframe e intraframe, como é o
caso do FrameNet Brasil. Para tornar a FrameNet Brasil capaz de realizar análises multimodais,
delineamos a hipótese de que, assim como as palavras em uma frase evocam frames e
organizam seus elementos na localidade sintática que os acompanha, os elementos visuais nos
planos de vídeo também podem evocar frames e organizar seus elementos na tela ou trabalhar
de forma complementar aos padrões de evocação de frames das sentenças narradas
simultaneamente ao seu aparecimento na tela, proporcionando diferentes perfis e opções de
perspectiva para a construção de sentido. O corpus anotado para testar a hipótese é composto
por episódios de um programa televisivo de viagens brasileiro aclamado pela crítica como um
exemplo de boas práticas em composição audiovisual. O gênero televisivo escolhido também
configura um novo conjunto experimental para a pesquisa em imagem integrada e compreensão
textual, uma vez que, neste corpus, o texto não é uma descrição direta da sequência de imagens,
mas se correlaciona com ela indiretamente em uma miríade de formas diversa. A Tese também
relata um experimento de rastreamento ocular realizado para validar a abordagem proposta para
uma anotação orientada por texto. O experimento demonstrou que não é possível determinar
que o texto impacta diretamente o direcionamento do olhar e foi tomado como um reforço para
a abordagem de valorização da combinação de modos. Por fim, apresentamos o conjunto de
dados Frame2, produto da tarefa de anotação realizada para o corpus seguindo a metodologia e
as diretrizes propostas. Os resultados obtidos demonstram que, pelo menos para esse gênero de
TV, mas possivelmente também para outros, uma anotação semântica refinada que aborde as
diversas correlações que ocorrem em um ambiente multimodal oferece uma nova perspectiva
na modelagem da compreensão multimodal. Além disso, a anotação multimodal também
enriquece o desenvolvimento de FrameNets, na medida em que as correlações encontradas entre
as modalidades podem atestar as escolhas de modelagem feitas por aqueles que criam recursos
baseados em frames.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio
Evolution and functions of human dance
Dance is ubiquitous among humans and has received attention from several disciplines. Ethnographic documentation suggests that dance has a signaling function in social interaction. It can influence mate preferences and facilitate social bonds. Research has provided insights into the proximate mechanisms of dance, individually or when dancing with partners or in groups. Here, we review dance research from an evolutionary perspective. We propose that human dance evolved from ordinary (non-communicative) movements to communicate socially relevant information accurately. The need for accurate social signaling may have accompanied increases in group size and population density. Because of its complexity in production and display, dance may have evolved as a vehicle for expressing social and cultural information. Mating-related qualities and motives may have been the predominant information derived from individual dance movements, whereas group dance offers the opportunity for the exchange of socially relevant content, for coordinating actions among group members, for signaling coalitional strength, and for stabilizing group structures. We conclude that, despite the cultural diversity in dance movements and contexts, the primary communicative functions of dance may be the same across societies
My Action, My Self: Recognition of Self-Created but Visually Unfamiliar Dance-Like Actions From Point-Light Displays
Previous research has shown that motor experience of an action can facilitate the visual recognition of that action, even in the absence of visual experience. We conducted an experiment in which participants were presented point-light displays of dance-like actions that had been recorded with the same group of participants during a previous session. The stimuli had been produced with the participant in such a way that each participant experienced a subset of phrases only as observer, learnt two phrases from observation, and created one phrase while blindfolded. The clips presented in the recognition task showed movements that were either unfamiliar, only visually familiar, familiar from observational learning and execution, or self-created while blind-folded (and hence not visually familiar). Participants assigned all types of movements correctly to the respective categories, showing that all three ways of experiencing the movement (observed, learnt through observation and practice, and created blindfolded) resulted in an encoding that was adequate for recognition. Observed movements showed the lowest level of recognition accuracy, whereas the accuracy of assigning blindfolded self-created movements was on the same level as for unfamiliar and learnt movements. Self-recognition was modulated by action recognition, as participants were more likely to identify themselves as the actor in clips they had assigned to the category “created” than in clips they had assigned to the category “learnt,” supporting the idea of an influence of agency on self-recognition
Efficient Human Activity Recognition in Large Image and Video Databases
Vision-based human action recognition has attracted considerable interest in recent research for its applications to video surveillance, content-based search, healthcare, and interactive games. Most existing research deals with building informative feature descriptors, designing efficient and robust algorithms, proposing versatile and challenging datasets, and fusing multiple modalities. Often, these approaches build on certain conventions such as the use of motion cues to determine video descriptors, application of off-the-shelf classifiers, and single-factor classification of videos. In this thesis, we deal with important but overlooked issues such as efficiency, simplicity, and scalability of human activity recognition in different application scenarios: controlled video environment (e.g.~indoor surveillance), unconstrained videos (e.g.~YouTube), depth or skeletal data (e.g.~captured by Kinect), and person images (e.g.~Flicker). In particular, we are interested in answering questions like (a) is it possible to efficiently recognize human actions in controlled videos without temporal cues? (b) given that the large-scale unconstrained video data are often of high dimension low sample size (HDLSS) nature, how to efficiently recognize human actions in such data? (c) considering the rich 3D motion information available from depth or motion capture sensors, is it possible to recognize both the actions and the actors using only the motion dynamics of underlying activities? and (d) can motion information from monocular videos be used for automatically determining saliency regions for recognizing actions in still images
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