Abstract
We present Attend-Fusion, a novel and efficient approach for audio-visual fusion in video classification tasks. Our method addresses the challenge of exploiting both audio and visual modalities while maintaining a compact model architecture. Through extensive experiments on the YouTube-8M dataset, we demonstrate that our Attend-Fusion achieves competitive performance with significantly reduced model complexity compared to larger baseline models.
Abstract (translated)
我们提出了Attend-Fusion，这是一种新颖且高效的视频分类任务中音视频融合方法。我们的方法解决了在保持紧凑模型架构的同时利用音频和视觉模态的挑战。通过在YouTube-8M数据集上的广泛实验，我们证明了与更大的基线模型相比，我们的Attend-Fusion在显著降低模型复杂度的情况下仍能实现具有竞争力的性能。
URL
https://arxiv.org/abs/2411.05603
