个人以为Swin Transformer最大的特点是类似于cnn中conv + pooling的结构。在Swin Transformer中,这种结构变成了Swin Transformer Block + Patch merging,通过多个stage,token数越来越少,每个token的感受野也会越来多大,同时由于token数的递减以及特殊设计的Window Transformer计算方式,减少了模型的计算量,可以说Swin Transforme...
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的速度精度折中,在不显著影响...
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...
which comprises four stages and performs two-times spatial downsampling in the patch merging layer of each stage. The major component in the new architecture is the Video Swin Transformer block, which consists of a 3D shifted window based multihead self-attention...
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, ...
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 ...
《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 ...
一、问题现象(附报错日志上下文): Video-Swin-Transformer模型转为onnx后,onnx模型无法进行推理,报错信息在onnxInferError.log日志文件中 尝试onnx转om模型,报错信息在onnx2om.log日志文件中 二、软件版本: -- CANN 版本 (e.g., CANN 3.0.x,5.x.x): ...
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...