例如,Alvareet al [156]从道路检测挑战中产生了323个图像的基础事实,3级,道路,垂直和天空。 10. Other Datasets are available for image segmentationpurposes too, such as Semantic Boundaries Dataset (SBD), PASCAL Part [158], SYNTHIA , and Adobe'sPortrait Segmentation....
* Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification* 链接: arxiv.org/abs/2207.0610* 作者: Matthias Rottmann,Marco Reese* 摘要: 在这项工作中,我们首次提出了一种用于检测具有语义分割图像数据集中标签错误的方法,即Pixel-Wise类标签。语义...
The proposed model is trained and tested using CamVid common dataset for semantic segmentation for self-driving missions. Extensive data augmentation has been done to the training set in order to overcome the problem of the small size of semantic segmentation datasets for autonomous driving. 2. Rela...
Further, with hierarchy-induced margin constraints, HSSN reshapes the pixel embedding space, so as to generate well-structured pixel representations and improve segmentation eventually. We conduct experiments on four semantic segmentation datasets (i.e., Mapillary Vistas 2.0, Cityscapes, LIP, and ...
56 papers with code • 9 benchmarks • 4 datasets Open-vocabulary semantic segmentation models aim to accurately assign a semantic label to each pixel in an image from a set of arbitrary open-vocabulary texts.Benchmarks Add a Result These leaderboards are used to track progress in Open ...
Language-Grounded Indoor 3D Semantic Segmentation in the Wild Implementation for our ECCV 2022 paper Abstract -Recent advances in 3D semantic segmentation with deep neural networks have shown remarkable success, with rapid performance increase on available datasets. However, current 3D semantic segmentation...
164 papers with code • 10 benchmarks • 8 datasets The semantic segmentation task is to assign a label from a label set to each pixel in an image. In the case of fully supervised setting, the dataset consists of images and their corresponding pixel-level class-specific annotations (...
This survey gives an overview over different techniques used for pixel-level semantic segmentation. Metrics and datasets for the evaluation of segmentation algorithms and traditional approaches for segmentation such as unsupervised methods, Decision Forests and SVMs are described and pointers to the relevant...
The Cityscapes Dataset is intended for assessing the performance of vision algorithms for major tasks of semantic urban scene understanding: pixel-level, instance-level, and panoptic semantic labeling; supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for trainin...
All the datasets above have had high impacts on the development of current state-of-the-art semanticsegmentation methods. However, there are few high-resolution semantic segmentation datasets based on UAV imagery with oblique views, such as (Nigam et al., 2018), which is supplemented with our ...