如Swin-Transformer仅仅通过移动窗口的方式来增加感受野,然而,每个Transformer块内的token依旧是有限的attention区域,需要堆叠更多的block来实现全局感受野。 这篇文章要介绍的CSWin Transformer(cross-shape window)是Swin-Transformer的改进版,它提出了通过十字形的窗口来做Self-attention,它不仅计算效率非常高,而且能够通过两...
【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...
另一个是局部增强位置编码,通过利用3*3深度卷积于V上,并将该结果直接添加到attention的结果上,以编码位置信息并为attention补充局部性。 1 Motivation Transformer由于其多头注意力机制强大的长程依赖建模能力,在需要高分辨率输入的视觉任务上取得了巨大的成功。然而,在全图计算attention是昂贵低效的。 一种方式是将self-...
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022 - microsoft/CSWin-Transformer
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 ...
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).
YOLOv8改进:引入CVPR 2023 BiFormer, 基于动态稀疏注意力构建高效金字塔网络架构,对小目标涨点明显 2023腾讯·技术创作特训营 第二期 背景:注意力机制是Vision Transformer的核心构建模块之一,可以捕捉长程依赖关系。然而,由于需要计算所有空间位置之间的成对令牌交互,这种强大的功能会带来巨大的计算负担和内存开销。为了...
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。
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 ...
CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped, CVPR 2022 - microsoft/CSWin-Transformer