SeMask: Semantically Masked Transformers for Semantic Segmentation(arxiv.org/pdf/2112.1278) 文章认为从预训练(Transformer based)backbone对语义分割网络进行finetune没有考虑到semantic priors。因此通过加入Semantic Layer将语义信息加入,得到更富有语义的特征。 具体过程如下(下图):对于Transformer架构的每个block(通常包...
文章地址:[2105.15203] SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers (arxiv.org) 代码地址:GitHub - NVlabs/SegFormer: Official PyTorch implementation of SegFormer SegFormer is a simple, efficient and powerful semantic segmentation method, as shown in Figure 1. We use...
我们提出了一种高效的实时语义分割双分辨率TransformerRTprorr,它比基于CNN的模型在性能和效率之间实现更好的权衡。为了在GPU这类设备上实现高推理效率,我们的RTformer利用了线性复杂度的GPU友好注意力,并抛弃了多头机制。此外,我们发现交叉分辨率注意通过传播从低分辨率分支获得的高级知识,可以更有效地收集高分辨率分支的...
论文:[2105.15203] SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers (arxiv.org) 代码:NVlabs/SegFormer: Official PyTorch implementation of SegFormer (github.com) 期刊/会议:NeurIPS 2021 摘要 我们提出了SegFormer,一个简单,高效而强大的语义分割框架,它将transformer与轻量级多层感...
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers论文阅读笔记 作者自己的解读比较精辟(NeurIPS'21) SegFormer: 简单有效的语义分割新思路 - Anonymous的文章 - 知乎 https://zhuanlan.zhihu.com/p/379054782 摘要 作者提出了基于Transformer的语义分割模型SegFormer,其有两个特点:层级式...
SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers PDF: https://arxiv.org/pdf/2105.15203.pdf PyTorch代码: https://github.com/shanglianlm0525/CvPytorch PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks...
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perceptron (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale ...
We present SegFormer, a simple, efficient yet powerful semantic segmentation framework which unifies Transformers with lightweight multilayer perception (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale ...
几篇论文实现代码:《SegViT: Semantic Segmentation with Plain Vision Transformers》(NeurIPS 2022) GitHub: github.com/zbwxp/SegVit [fig1] 《Training-Free Structured Diffusion Guidance for Compositio...
Code of CVPR 2022 paper: Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers. [arXiv][Project][Poster] Abastract Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important and challenging task. Due to the high training ...