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...
论文地址:Video Swin Transformer 代码地址:https://github.com/SwinTransformer/Video-Swin-Transformer 文章也是做视频分类的上来就是各种第一,非常的朴实无华。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...
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...
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, ...
《Video Swin Transformer》(2021) GitHub:https:// github.com/SwinTransformer/Video-Swin-Transformer [fig1]【转发】@爱可可-爱生活:几篇论文实现代码:《Diverse Branch Block: Building a Convolution as ...
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...
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 ...
swin_base_patch244_window1677_sthv2 Notes The input shape for these models are [None, 3, 32, 224, 224] representing [batch_size, channels, frames, height, width]. To create models with different input shape use this notebook. References [1] Video Swin Transformer Ze et al. [2] Video...
具体而言,视频Swin Transformer遵循Swin Transformer的设计,是一个由四个阶段组成的层次结构。在每两个阶段之间,通过patch合并层执行空间下采样,这将关联每组2×2空间相邻patch的特征。下采样后,线性层将每个concatenated token的特征映射到其维数的一半。在应用特征变换之后,会出现一系列Swin attention blocks。