因为Transformer是输入顺序无关的,因此需要向其中加入位置编码。上图左边为ViT模型的PE,使用的绝对位置编码或者是条件位置编码,只在embedding的时候与token一起进入transformer,中间的是Swin,CrossFormer等模型的PE,使用相对位置编码偏差,通过引入token图的权重来和attention一起计算,灵活度更好相对APE效果更好。 本文所提出...
【CVPR2022】CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows 论文:https://arxiv.org/abs/2107.00652 代码:https://github.com/microsoft/CSWin-Transformer 1、Motivation 这个论文的想法是受了 CCNet 的启发,CCNet 是认为注意力计算过于复杂,因此提出 criss-cross 的注意力计算...
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022 - microsoft/CSWin-Transformer
The code and models will be available at this https URL. (在新选项卡中打开) Publication 活动 CVPR 2022 研究领域 Computer vision 关注我们: 关注X 在Facebook关注 关注LinkedIn 在Youtube上订阅 关注Instagram 订阅本站 RSS 分享此页: 分享到 X 分...
May 6, 2022 install.sh first upload May 6, 2022 README MIT license CSWinTT The official implementation of the CVPR 2022 paperTransformer Tracking with Cyclic Shifting Window Attention [Models and Raw results](Google Driver) or[Models and Raw results](Baidu Driver: bsa2). ...
具体来说,传统的CNN在处理长序列时缺乏全局性,而ViT虽然具有全局性,但它们的注意力机制复杂,导致计算量大且效率低下。为了克服这些缺点,Next-ViT引入了Next Convolution Block(NCB)和Next Transformer Block(NTB),并设计了Next Hybrid Strategy(NHS)来提高模型的性能。
The code and models will be available at this https URL. Opens in a new tab Publication Events CVPR 2022 Research Areas Computer vision Follow us: Follow on X Like on Facebook Follow on LinkedIn Subscribe on Youtube Follow on Instagram...
We present CSWin Transformer, an efficient and effective Transformer-based backbone for general-purpose vision tasks. A challenging issue in Transformer design is that global self-attention is very expensive to compute whereas local self-attention often limits the field of interactions of each token....
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022 - microsoft/CSWin-Transformer