由于Video Swin Transformer改编于Swin Transformer,因此Video Swin Transformer可以用在大型图像数据集上预训练的模型参数进行初始化。与Swin Transformer相比,Video Swin Transformer中只有两个模块具有不同的形状,分别为:线性embedding层和相对位置编码。 输入token在时间维度上变成了2,因此线性embedding层的形状从Swin Transf...
和Swin Transformer是一个团队的工作。 可以先看下Swin Transformer:下雨前:Swin-transformer的理解和代码(torch.roll) 摘要 作者提倡使用局部性的归纳偏置在视频Transformer中,可以更好地平衡速度和精确度。也是使用了空间-时间因式分解的注意力。局部性的是现实通过图片的Swin-transformer学习的。 在K-400上的top-1...
1. 首先运行:python tools/test.py configs/recognition/swin/swin_base_patch244_window877_kinetics400_1k.py model/swin_base_patch244_window877_kinetics400_1k.pth --eval top_k_accuracy 遇到错误:File &q... 查看原文 I3D阅读笔记 I3D阅读笔记 Paper:Quo Vadis, Action Recognition? A New Model an...
由于Video Swin Transformer改编于Swin Transformer,因此Video Swin Transformer可以用在大型图像数据集上预训练的模型参数进行初始化。与Swin Transformer相比,Video Swin Transformer中只有两个模块具有不同的形状,分别为:线性embedding层和相对位置...
The locality of the proposed video architecture is realized by adapting the Swin Transformer designed for the image domain, while continuing to leverage the power of pre-trained image models. Our approach achieves state-of-the-art accuracy on a broad range of video recognition benchmarks, ...
The model innovatively integrates Video Swin Transformer into the generator of generative adversarial network (GAN). Specifically, the generator of the model employs convolutional neural network (CNN) to extract shallow features, and utilizes the Video Swin Transformer to extract deep multi-scale ...
Short Description Video Swin Transformer is a pure transformer based video modeling algorithm, attained top accuracy on the major video recognition benchmarks. Papers https://arxiv.org/abs/2106.13230 published in 2021, Cited by 1154 (unt...
Video Swin Transformer achieved 84.9 top-1 accuracy on Kinetics-400, 86.1 top-1 accuracy on Kinetics-600 with ∼20× less pre-training data and ∼3× smaller model size, and 69.6 top-1 accuracy on Something-Something v2. The results demonstrate the superior performance of t...
论文:Video Swin Transformer 代码:Video-Swin-Transformer 动机 基于CNN的方法的潜力受到卷积算子感受野小的限制 自注意力机制可以用更少的参数和更低的计算成本来扩大感受野,因此纯transformer网络在主流视频识别benchmark上取得佳绩 针对联合时空建模既不经济又不容易优化的问题,前人提出了时空域因式分解的方法以达到更好...