CSWin Transformer最核心的部分就是cross-shaped window self-attention,如下所示,首先将self-attention的mutil-heads均分成两组,一组做horizontal stripes self-attention,另外一组做vertical stripesself-attention。 所谓horizontal stripes self-attention就是沿着H维度将tokens分成水平条状windows,对于输入为HxW的tokens,记...
Transformer落地Bayesian思想的时候权衡多种因素而实现最大程度的近似估计Approximation,例如使用了计算上相对CNN、RNN等具有更高CPU和内存使用性价比的Multi-head self-attention机制来完成更多视角信息集成的表达,在Decoder端训练时候一般也会使用多维度的Prior信息完成更快的训练速度及更高质量的模型训练,在正常的工程落地中...
Thus, this paper proposes a novel Local Self-Attention in Transformer (LSAT) for a visual question answering model to address these issues. The LSAT model simultaneously models intra-window and inter-window attention by setting local windows for visual features. Therefore, the LSAT model can ...
we propose a local self-attention which considers a moving window over the document terms and for each term attends only to other terms in the same window. This local attention incurs a fraction of the compute and memory cost of attention over the whole document. The windowed approach also le...
1. Local Attention是什么? 2020年的ViT横空出世,席卷了模型设计领域,铺天盖地的各种基于Transformer的结构开始被提出,一些在卷积神经网络中取得成功的先验知识,如local operation、多尺度、shuffled等等各种操作和inductive bias被引入Transformer之...
*Embedded Gaussian操作与self-attention很类似,实际上,self-attention是其一个特例。但是作者认为,这种注意力不是不可或缺的,f函数的表现形式还可以有下列两种: Dot product 通过点乘进行相似度计算: 归一化因子可以直接设置为N,也就是X的所有位置数。
Hi, I am wondering if there is any way to change the stride of the local attention window. For example, i-th query attends to keys in [i * stride + seqlen_q - seqlen_k + win_size[0], i * stride + seqlen_q - seqlen_k + win_size[1]]...
NA is just a subgraph of the self attention graph. There are some obvious disadvantages of this compared to Natten. Specifically, This has quadratic computation cost wrt height * width. It must materialize the entire attention mask. But, if we use some variant of sliding-window attention, we...
Focal Self-Attention的结构如上图所示,首先明确三个概念: Focal levels :可以表示FSA中对特征关注的细粒度程度。level L的下标越小,对特征关注也就越精细。 Focal window size :作者将token划分成了多个sub-window,focal window size指的是每个sub-window的大小。
*Embedded Gaussian操作与self-attention很类似,实际上,self-attention是其一个特例。但是作者认为,这种注意力不是不可或缺的,f函数的表现形式还可以有下列两种: Dot product 通过点乘进行相似度计算: 归一化因子可以直接设置为N,也就是X的所有位置数。