因此,Attention机制对长序列处理能力有限。 根据[1]中的内容,我们将现有的Efficient Attention分为五类: Local Attention Hierarchical Attention Sparse Attention Approximated Attention IO-Aware Attention Local Attention 几种典型的Local casual Attention[1] Local Attention的主要改进是不在对除本身外所有tokens进行...
In order to address these limitations, this paper introduces an Efficient Local Attention (ELA) method that achieves substantial performance improvements with a simple structure. By analyzing the limitations of the Coordinate Attention method, we identify the lack of generalization ability in Batch ...
大多数的注意力机制通过dot-product 来计算需要大量的内存和计算资源这限制了在高分辨率图像上的应用。 本文提出了一种与dot-product 注意等价的高效注意机制,但大大减少了内存和计算成本。以Non-local module 为…
@InProceedings{Arar_2022_CVPR, author = {Arar, Moab and Shamir, Ariel and Bermano, Amit H.}, title = {Learned Queries for Efficient Local Attention}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022} } ...
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...
If not (-1, -1), implements sliding window local attention. Return: out: (batch_size, seqlen, nheads, headdim). """ def flash_attn_with_kvcache( q, k_cache, v_cache, k=None, v=None, rotary_cos=None, rotary_sin=None, cache_seqlens: Optional[Union[(int, torch.Tensor)]] =...
Efficient Spatial Attention Block. We know that the local feature map \({\mathbf{anchor}}_{local}\) is put into channel attention and spatial attention blocks. Then in the efficient spatial attention block the local feature map \({\mathbf{anchor}}_{local}\) is applied with Maxpool and Av...
Attention Guided Multi-Scale Regression for Scene Text Detection A large number of neural network models have been applied to this task, one of which is a fully convolutional network (FCN) model named An Efficient and Accurate Scene Text Detector (EAST). However, it usually falls short when ....
例如,Sparse Transformer 将其一半的头部分配给模式,结合strided 和 local attention。 类似地,Axial Transformer 在给定高维张量作为输入的情况下,沿着输入张量的单轴应用一系列的self attention计算。 本质上,模式组合以固定模式相同的方式降低了内存的复杂度。 但是,不同之处在于,多模式的聚集和组合改善了self attenti...
具体方向包括Sparse Attention Patterns(解决超长的文本比较有效,local attention、block-wise attention)、Memory Saving Designs(reduce dimension、multi-query attention等,multi-query attention在不同head内共享keys和values)、Adaptive Attention(为每个token在每个head上自适应的学习更稀疏有效的attention而非full attention...