语义分割的运算量与像素量正相关,因此对于高清图像来说,运算量巨大。 However, the computational cost of general semantic segmentation models increases linearly to the number ofpixels. Thus, semantic segmentation for high-resolution images has a heavy computational cost, which leads to its inapplicable of ...
本篇是发表在 CVPR 2022 上的 Generalized Few-shot Semantic Segmentation(后文简称 GFS-Seg),既一种泛化的小样本语义分割模型。在看论文的具体内容之前,我们先了解一些前置知识。 深度学习是 Data hunger 的方法, 需要大量的数据,标注或者未标注。少样本学习研究就是如何从少量样本中去学习。拿分类问题来说,每个...
解读:https://mp.weixin.qq.com/s/MsvRpR_r2X-BtcXfEFIm7A Current Knowledge Distillation (KD) methods for semantic segmentation often guide the student to mimic the teacher’s structured information generated from individual data samples. However, they ignore the global semantic relatio...
之前已经有过关于小样本语义分割的论文解读,关于如何用 Transformer 思想的分类器进行小样本分割,链接见:https://mp.weixin.qq.com/s/YVg8aupmAxiu5lGTYrhpCg 。本篇是发表在 CVPR 2022 上的 Generalized Few-shot Semantic Segmentation(后文简称 GFS-Seg),既一种泛化的小样本语义分割模型。在看论文的具体内容之...
Robust Prototypical Few-Shot Organ Segmentation With Regularized Neural-ODEs -based methods tend to be extremely vulnerable to adversarial attacks, R-PNODE exhibits increased adversarial robustness for a wide array of these attacks... P Pandey,M Chasmai,SB Lall - 《IEEE Transactions on Medical Ima...
[2022-10-12] Repo created. Code will come soon. Stay tuned. Abstract Prevalent semantic segmentation solutions are, in essence, a dense discriminative classifier of p(class|pixel feature). Though straightforward, this de facto paradigm neglects the underlying data distribution p(pixel feature|class...
The present disclosure relates to a semantic segmentation method and system for a remote sensing image fusing GIS data. The method includes: obtaining a first remote sensing data training set and first GIS data; preprocessing the first remote sensing data training set to obtain a second remote ...
Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of real-time methods is not satisfactory. In this work, we propose PP-LiteSeg, a novel lightweight model for the real-time...
【NeurIPS 2022】SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation 1、研究动机 来自清华大学国孟昊博士的论文,可以理解为大核卷积 large kernel attention 的扩展,该方法是在 Visual Attention Network 这篇论文中提出,思想是:用 大核卷积 来替换 Transformer 模型中的 attention。具体如下图...
【CVPR2022】Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation 代码:https://github.com/facebookresearch/HRViT 核心思想和主要方法 这个论文的核心思想就是将 HRNet 和 Transformer 相结合,同时,为了应用于密集预测任务,提出了避免计算复杂度过高的解决方案。