IPSA 为Inner-Patch Self-Attention,意思就是在每个 Patch 内部做 Attention,特征通道数作为 Attention 特征维度,大小为每个 Patch 中的特征个数,例如 7*7=49 个。在不同 Patch 间不产生关联,可以表示为下图: 由于从传统 H*W 大小的 Attention 变为 Patch 覆盖的面积啊大小,复杂度明显减少。这部分代码很容易实...
这边我们简单看一下cross-attention的代码实现: classCrossAttention(nn.Module):def__init__(self,dim,num_heads=8,qkv_bias=False,qk_scale=None,attn_drop=0.,proj_drop=0.):super().__init__()self.num_heads=num_headshead_dim=dim//num_heads# NOTE scale factor was wrong in my original vers...
理想的情况下,我们希望将每一个像素都视作一个token,但是计算量巨大,受到CNN局部特征提取特性的启发,我们将CNN的局部卷积方法引入到了Transformer中,在每个单独的patch中逐像素的计算self-attetion,就是文中的Inner-Patch Self-Attention (IPSA),我们把一个局部当作一个注意范围,而不是整个画面。同时,Transformer可以...
Arxiv 2106 - CAT: Cross Attention in Vision Transformer 论文:https://arxiv.org/abs/2106.05786 代码:https://github.com/linhezheng19/CAT 详细解读:https://mp.weixin.qq.com/s/VJCDAo94Uo_OtflSHRc1AQ 核心动机:使用patch内部和patch之间attention简化了...
论文地址:[2108.00154] CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention (arxiv.org) 代码地址:https://github.com/cheerss/CrossFormer 一、Motivation 主要还是ViT的历史遗留问题 ViT在处理输入时,将图片划分为了相等大小的图像块(Patch),然后通过linear操作生成token序列,这种操作导致Vi...
This is official implement of"CAT: Cross Attention in Vision Transformer". Abstract Since Transformer has found widespread use in NLP, the potential of Transformer in CV has been realized and has inspired many new approaches. However, the computation required for replacing word tokens with image pa...
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几篇论文实现代码:《Cross-Episodic Curriculum for Transformer Agents》(NeurIPS 2023) GitHub: github.com/CEC-Agent/CEC [fig7]《Latent Graph Inference with Limited Supervision》(NeurIPS 2023) GitHub: github.com/Jianglin954/LGI-LS《Towards Real-Time 4K Image Super-Resolution》(CVPR 2023) GitHub: ...
""" Vision Transformer with support for patch or hybrid CNN input stage """ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate...
MLP as used in Vision Transformer, MLP-Mixer and related networks """ def __init__(self, in_features, hidden_features=None, ffn_expand_factor=2, bias=False): super().__init__() hidden_features = int(in_features*ffn_expand_factor) self.project_in = nn.Conv2d( in_...