"A computational aesthetic design science study on online video based o" by Zhangguang KANG, Fiona Fui-hoon NAH et al.
 

Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

7-2024

Abstract

Computational video aesthetic prediction refers to using models that automatically evaluate the features of videos to produce their aesthetic scores. Current video aesthetic prediction models are designed based on bimodal frameworks. To address their limitations, we developed the Triple-Dimensional Multimodal Temporal Video Aesthetic neural network (TMTVA-net) model. The Long Short-Term Memory (LSTM) forms the conceptual foundation for the design framework. In the multimodal transformer layer, we employed two distinct transformers: the multimodal transformer and the feature transformer, enabling the acquisition of modality-specific patterns and representational features uniquely adapted to each modality. The fusion layer has also been redesigned to compute both pairwise interactions and overall interactions among the features. This study contributes to the video aesthetic prediction literature by considering the synergistic effects of textual, audio, and video features. This research presents a novel design framework that considers the combined effects of multimodal features.

Keywords

Computational Video Aesthetic, Multimodal Analysis, Neural Network, Design Science

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

HCI International 2024: Late breaking papers: Washington, DC, June 29 - July 4

Volume

15380

First Page

68

Last Page

79

ISBN

9783031768217

Identifier

10.1007/978-3-031-76821-7_6

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/978-3-031-76821-7_6

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