Abstract
In this paper, we present a toolchain for a comprehensive audio/video analysis by leveraging deep learning based multimodal approach. To this end, different specific tasks of Speech to Text (S2T), Acoustic Scene Classification (ASC), Acoustic Event Detection (AED), Visual Object Detection (VOD), Image Captioning (IC), and Video Captioning (VC) are conducted and integrated into the toolchain. By combining individual tasks and analyzing both audio & visual data extracted from input video, the toolchain offers various audio/video-based applications: Two general applications of audio/video clustering, comprehensive audio/video summary and a specific application of riot or violent context detection. Furthermore, the toolchain presents a flexible and adaptable architecture that is effective to integrate new models for further audio/video-based applications.
| Original language | English |
|---|---|
| Title of host publication | 2024 International Conference On Content-based Multimedia Indexing, Cbmi |
| Pages | 349-352 |
| Number of pages | 4 |
| ISBN (Electronic) | 979-8-3503-7844-3 |
| DOIs | |
| Publication status | Published - Feb 2025 |
| Event | 21st International Conference on Content-based Multimedia Indexing - Reykjavik University (RU), Reykjavik, Iceland Duration: 18 Sept 2017 → 20 Sept 2024 https://cbmi2024.org/ |
Conference
| Conference | 21st International Conference on Content-based Multimedia Indexing |
|---|---|
| Abbreviated title | CBMI 2024 |
| Country/Territory | Iceland |
| City | Reykjavik |
| Period | 18/09/17 → 20/09/24 |
| Internet address |
Research Field
- Multimodal Analytics
Keywords
- Deep learning model
- multimodal
- toolchain
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