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Multi-Source Transformer Architectures for Audiovisual Scene Classification

2022-10-18 23:42:42
Wim Boes, Hugo Van hamme
arXiv_CV
         
Abstract

In this technical report, the systems we submitted for subtask 1B of the DCASE 2021 challenge, regarding audiovisual scene classification, are described in detail. They are essentially multi-source transformers employing a combination of auditory and visual features to make predictions. These models are evaluated utilizing the macro-averaged multi-class cross-entropy and accuracy metrics. In terms of the macro-averaged multi-class cross-entropy, our best model achieved a score of 0.620 on the validation data. This is slightly better than the performance of the baseline system (0.658). With regard to the accuracy measure, our best model achieved a score of 77.1\% on the validation data, which is about the same as the performance obtained by the baseline system (77.0\%).

Abstract (translated)

URL

https://arxiv.org/abs/2210.10212

PDF

https://arxiv.org/pdf/2210.10212.pdf

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