既然是为了解决类与类之间的决策边界不明显的问题,那么我在分割中引入更多的显式类信息不就好了,所以作者的论文题目叫Semantic-Aware。所谓semantic,说白了就是类。semantic-aware说人话就是有较为明确的目标类信息给你支撑分割。 但是分割前,哪里来的显式类信息,答曰:伪标签 那么作者是怎么显式引入目标类信息的了。
ICASSP 2023:DOMAIN GENERALIZED FUNDUS IMAGE SEGMENTATION VIA DUAL-LEVEL MIXING 摘要:输入级和特征级混合策略(DLM),增强数据的多样性,提高泛化性能 输入层: 使用不同的数据增强策略,从不同数据增强结果的不同空间位置上去一部分, mix 成最终的结果 特征层: 1、原始输入特征的归一化 2、使用原始特征的均值和标准...
Domain-generalized few-shot text classification (DG-FSTC) is a new setting for few-shot text classification (FSTC). In DG-FSTC, the model is meta-trained on a multi-domain dataset, and meta-tested on unseen datasets with different domains. However, previous methods mostly construct semantic ...
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentationarxiv.org/abs/2204.02548 在这项工作中,我们提出了风格幻觉双一致性学习(SHADE)框架来处理这种领域转换。具体来说,SHADE是基于两个一致性约束构建的,即风格一致性(SC)和回顾一致性(RC)。此外,我们提出了一种新的风格幻觉模...
Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited ...
Adversarial Semantic Hallucination for Domain Generalized Semantic Segmentation,获取车牌轮廓上的点集后,可用cv2.minAreaRect()获取点集的最小外接矩形。返回值rect内包含该矩形的中心点坐标、高度宽度及倾斜角度等信息,使用cv2.boxPoints()可获取该矩形的四个顶点
论文标题: Stronger, Fewer, & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation 论文地址:https://arxiv.org/pdf/2312.04265.pdf 代码:https://github.com/w1oves/Rein.git 发表于:CVPR 2024 作者起名的一些巧思: ...
The different reconstruction parameters of CT imaging lead to domain shifts, which limits the generalization of deep learning models and their applications in computer-aided diagnosis systems. In this paper, we investig...
This repository contains the code for the paper: Collaborating Foundation models for Domain Generalized Semantic Segmentation.OverviewDomain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existi...
Domain generalization~(DG) aims at solving distribution shift problems in various scenes. Existing approaches are based on Convolution Neural Networks (CNNs) or Vision Transformers (ViTs), which suffer from limited receptive fields or quadratic complexities issues. Mamba, as an emerging state space mo...