English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we present a deep learning based multimodal system forclassifying daily life videos. To train the system, we propose a two-phasetraining strategy. In the first training phase (Phase I), we extract the audioand visual (image) data from the original video. We then train the audio dataand the visual data with independent deep learning based models. After thetraining processes, we obtain audio embeddings and visual embeddings byextracting feature maps from the pre-trained deep learning models. In thesecond training phase (Phase II), we train a fusion layer to combine theaudio/visual embeddings and a dense layer to classify the combined embeddinginto target daily scenes. Our extensive experiments, which were conducted onthe benchmark dataset of DCASE (IEEE AASP Challenge on Detection andClassification of Acoustic Scenes and Events) 2021 Task 1B Development,achieved the best classification accuracy of 80.5%, 91.8%, and 95.3% with onlyaudio data, with only visual data, both audio and visual data, respectively.The highest classification accuracy of 95.3% presents an improvement of 17.9%compared with DCASE baseline and shows very competitive to the state-of-the-artsystems.

Deep Learning Based Multimodal with Two-phase Training Strategy for Daily Life Video Classification