Vision Transformers for Dense Prediction 论文链接:https://arxiv.org/abs/2103.13413v1论文代码:https://github.com/isl-org/DPT Abstract 本文引入dense vision transformers,它用vision transformers 代替卷积网络作为密集预测(dense prediction)任务的主干。将来自 Vision Transformer 各个阶段的token组装成各种分辨率的...
整体Transformer 结构可以看下面代码: defforward(self,x):# 这就是普通 ViT,中间选取了四层layer_1,layer_2,layer_3,layer_4=forward_vit(self.pretrained,x)# 重组结构layer_1_rn=self.scratch.layer1_rn(layer_1)layer_2_rn=self.scratch.layer2_rn(layer_2)layer_3_rn=self.scratch.layer3_rn(laye...
CVPR2022 | MPViT: Multi-Path Vision Transformer for Dense Prediction 论文:https://arxiv.org/abs/2112.11010 代码:https://github.com/youngwanLEE/MPViT 主要内容 做了点啥 本文重点探究Transformer中的multi-scale patch embedding和multi-path structure scheme的设计。 主要...
以ViT为backbone的dense vision transformers,主要由三个部分组成:transformer encoder、convolutional decoder、fusion 与ViT中使用的bag-of-words表示类似,DPT首先将图像划分成大小为 p^2 的patch,其次通过Embed(线性映射或ResNet)转换至特征空间,每一个“word”视为一个token,token与patch一一对应(未改变分辨率),经过...
Vision Transformers for Dense Prediction Rene´ Ranftl Alexey Bochkovskiy Intel Labs rene.ranftl@intel.com Vladlen Koltun Abstract We introduce dense prediction transformers, an archi- tecture that leverages vision transformers in place of con- volutional networks as a backbone for dense prediction...
该架构包括了将读出标识插入分块、ViT网络处理、连接与MLP的尺度变化融合，以及RefineNet-based模块的融合部分。DPT-Large、DPT-Base和DPT-Hybrid三种模型的不同之处在于ViT中重组连接层的设定，展示了Transformer在深度估计领域的早期尝试。尽管结构相对直观，但实验部分提到的“任意大小图片输入”并非独家创新...
We introduce dense vision transformers, an architecture that leverages vision transformers in place of convolutional networks as a backbone for dense prediction tasks. We assemble tokens from various stages of the vision transformer into image-like representations at various resolutions and progressively com...
代码地址: 3.MViTv2: Improved Multiscale Vision Transformers for Classification and Detection 在本文中,我们研究了多尺度视觉转换器 (MViTv2) 作为图像和视频分类以及目标检测的统一架构。我们提出了 MViT 的改进版本,它结合了分解的相对位置嵌入和残差池连接。我们将该架构实例化为五种尺寸,并针对 ImageNet 分类...
Vision transformers for dense prediction: A survey-ELSEVIER 2022.Semantic segmentation using Vision ...
^Pyramid vision transformer: A versatile backbone for dense prediction without convolutions ^Twins: Revisiting the design of spatial attention in vision transformers ^Coatnet: Marrying convolution and attention for all data sizes ^Mobilevit: light-weight, general-purpose, and mobile-friendly vision trans...