Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data. However, bypassing iterative distillation by directly ...
Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation Dipam Goswami†§ Rene´ Schuster† Joost van de Weijer‡ Didier Stricker† dipamgoswami01@gmail.com rene.schuster@dfki.de joost@...
Class-Incremental Semantic Segmentation (CISS) aims to learn new classes without forgetting the old ones, using only the labels of the new classes. To achieve this, two popular strategies are employed: 1) pseudo-labeling and knowledge distillation to preserve prior knowledge; and 2) background ...
train_erfnet_incremental.py new file: .gitignore Aug 8, 2023 train_erfnet_static.py new file: .gitignore Aug 8, 2023 Repository files navigation README License Few-shot Class-Incremental Semantic Segmentation via Pseudo-Labeling and Knowledge Distillation ...
上传于:2024-11-08 粉丝量:3 Nothing Is All. 下载此文档 SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning 下载积分: 199 内容提示: 文档格式:PDF | 页数:18 | 浏览次数:4 | 上传日期:2024-11-08 00:15:18 | 文档星级: 阅读...
本文提出了一种基于回放的类增量学习(Class-Incremental Learning, CIL)算法,通过利用类别激活图(CAM)生成掩码来压缩旧类别的示例图像,从而在有限的内存预算下保存更多的示例。关键步骤包括:1) 使用CAM生成0-1掩码以定位图像中的判别性像素;2) 通过类增量掩码(CIM)模型自适应地优化掩码生成过程;3) 利用双层优化问题...
(2021) proposed the first attempt to solve incremental few-shot semantic segmentation. They proposed PIFS, which combines prototype learning with knowledge distillation. In the base stage, PIFS trains the network on base data to develop the capability of feature extraction. In the FSL stage, PIFS...
fast framework for few-shot class-incremental learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. [61] Hanbin Zhao, Fengyu Yang, Xinghe Fu, and Xi Li. Rbc: Rec- tifying the biased context in continual semantic segmentation. In ECCV, pages...
将class incremental learning转化为task incremental learning,每个task相当于为每个标签y学一个类别条件生成模型。 Method 作者使用VAE作为每个类别要学习的生成式分类器,通过重要性采样估计似然p(x|y)p(x|y),使用均匀分布建模p(y)p(y)。 VAE包含一个encoderqϕqϕ,将输入变为隐空间的后验分布qϕ(z|x)...
论文地址: https://arxiv.org/abs/2004.00440 目录 一、贡献点 二、方法 2.1 triple loss 2.2 NCM(nearest class mean)分类器 2.3 Semantic Drift Compensation 三、实验及验证 3.1 SDC的作用 3.2 NCM及triple-loss 3.3 准确率 四、总结 一、贡献点 文章发表于CVPR2... 查看原文 [arxiv 20200628] Few-Shot...