Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions
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Abstract (arXiv)
This paper presents the details of the Audio-Visual Scene Classification task in the DCASE 2021 Challenge (Task 1 Subtask B). The task is concerned with classification using audio and video modalities, using a dataset of synchronized recordings. This task has attracted 43 submissions from 13 different teams around the world. Among all submissions, more than half of the submitted systems have better performance than the baseline. The common techniques among the top systems are the usage of large pretrained models such as ResNet or EfficientNet which are trained for the task-specific problem. Fine-tuning, transfer learning, and data augmentation techniques are also employed to boost the performance. More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams. The best system among all achieved a logloss of 0.195 and accuracy of 93.8%, compared to the baseline system with logloss of 0.662 and accuracy of 77.1%.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 May 2021.
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Citing papers per year: 2022: 3.
3 papers.
- 2022Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classificationcited by 5
- 2022Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategycited by 0
- 2022Multi-Source Transformer Architectures for Audiovisual Scene Classificationcited by 0
- 2022Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification31 Dec 2022
- 2022Multi-Source Transformer Architectures for Audiovisual Scene Classification18 Oct 2022
- 2022Audio-Visual Scene Classification Using A Transfer Learning Based Joint Optimization Strategy25 Apr 2022