论文标题
视频识别的多视图变压器
Multiview Transformers for Video Recognition
论文作者
论文摘要
视频理解需要在多个时空的分辨率下进行推理 - 从短粒子动作到较长持续时间发生的事件。尽管变压器架构最近推进了最新的架构，但它们尚未明确建模不同的时空分辨率。为此，我们介绍了视频识别（MTV）的多视图变压器。我们的模型由单独的编码器组成，以表示输入视频的不同视图，并带有横向连接以跨视图融合信息。我们介绍了对我们的模型的彻底消融研究，并表明MTV在各种型号范围内的准确性和计算成本方面始终如一地表现优于单视图。此外，我们在六个标准数据集上实现了最先进的结果，并通过大规模预处理进一步改善。代码和检查点可在以下网址提供：https：//github.com/google-research/scenic/tree/main/main/scenic/projects/mtv。
Video understanding requires reasoning at multiple spatiotemporal resolutions -- from short fine-grained motions to events taking place over longer durations. Although transformer architectures have recently advanced the state-of-the-art, they have not explicitly modelled different spatiotemporal resolutions. To this end, we present Multiview Transformers for Video Recognition (MTV). Our model consists of separate encoders to represent different views of the input video with lateral connections to fuse information across views. We present thorough ablation studies of our model and show that MTV consistently performs better than single-view counterparts in terms of accuracy and computational cost across a range of model sizes. Furthermore, we achieve state-of-the-art results on six standard datasets, and improve even further with large-scale pretraining. Code and checkpoints are available at: https://github.com/google-research/scenic/tree/main/scenic/projects/mtv.
