What Modality Matters? Exploiting Highly Relevant Features for Video Advertisement Insertion


Chong, Onn Keat and Goh, Hui Ngo and See, John Su Yang (2023) What Modality Matters? Exploiting Highly Relevant Features for Video Advertisement Insertion. In: 2023 IEEE International Conference on Image Processing (ICIP), 08-11 October 2023, Kuala Lumpur, Malaysia.

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Video advertising is a thriving industry that has recently turned its attention to the use of intelligent algorithms for automating tasks. In advertisement insertion, the integration of contextual relevance is essential in influencing the viewer’s experience. Despite the wide spectrum of audio-visual semantic modalities available, there is a lack of research that analyzes their individual and complementary strengths in a systematic manner. In this paper, we propose an ad-insertion framework that maximizes the contextual relevance between advertisement and content video by employing high-level multi-modal semantic features. Prediction vectors are derived via clip-level and image-level extractors, which are then matched accordingly to yield relevance scores. We also established a new user study methodology that produces gold standard annotations based on multiple expert selections. By comprehensive human-centered approaches and analysis, we demonstrate that automatic ad-insertion can be improved by exploiting effective combinations of semantic modalities.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: video advertising, advertisement insertion, feature extraction, human-centered computing
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 23 Feb 2024 04:20
Last Modified: 23 Feb 2024 04:20
URII: http://shdl.mmu.edu.my/id/eprint/12127


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