论文标题
视听场景分类的多源变压器体系结构
Multi-Source Transformer Architectures for Audiovisual Scene Classification
论文作者
论文摘要
在这份技术报告中，我们针对Dcase 2021挑战的子任务1B提交了有关视听场景分类的系统。它们本质上是多源变压器，采用听觉和视觉特征的组合来做出预测。利用宏观平均的多级跨境和准确度指标来评估这些模型。 就宏观平均的多级跨境而言，我们的最佳模型在验证数据上的得分为0.620。这比基线系统的性能要好得多（0.658）。 关于准确度度量，我们的最佳模型在验证数据上达到了77.1 \％的得分，这与基线系统获得的性能大致相同（77.0 \％）。
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\%).
