In this paper, we propose a solution for improving the quality of temporal sound localization. We employ a multimodal fusion approach to combine visual and audio features. High-quality visual features are extracted using a state-of-the-art self-supervised pre-training network, resulting in efficient video feature representations. At the same time, audio features serve as complementary information to help the model better localize the start and end of sounds. The fused features are trained in a multi-scale Transformer for training. In the final test dataset, we achieved a mean average precision (mAP) of 0.33, obtaining the second-best performance in this track.

翻译：本文提出了一种提升时序声音定位质量的解决方案。我们采用多模态融合方法，结合视觉与音频特征。通过使用先进的自监督预训练网络提取高质量视觉特征，从而获得高效的视频特征表示。同时，音频特征作为补充信息，帮助模型更准确地定位声音的起始与结束时刻。融合后的特征在多尺度Transformer中进行训练。在最终测试数据集上，我们取得了0.33的平均精度均值（mAP），在该赛道中获得了第二名的性能。