Mseg
2D Box Tracking
2D Polygon
Autonomous Driving
|...
许可协议: Unknown

Overview

We present MSeg, a composite dataset that unifies se- mantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.

Citation

Please use the following citation when referencing the dataset:

@InProceedings{MSeg_2020_CVPR,
author = {Lambert, John and Zhuang, Liu and Sener, Ozan and Hays, James and Koltun, Vladlen},
title = {{MSeg}: A Composite Dataset for Multi-domain Semantic Segmentation},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}
数据概要
数据格式
Image,
数据量
80K
文件大小
--
发布方
John Lambert
A Ph.D. student at Georgia Tech, completed Bachelor’s and Master’s degrees in Computer Science at Stanford University in 2018, specializing in artificial intelligence.
数据集反馈
| 10 | 数据量 80K | 大小 --
Mseg
2D Box Tracking 2D Polygon
Autonomous Driving
许可协议: Unknown

Overview

We present MSeg, a composite dataset that unifies se- mantic segmentation datasets from different domains. A naive merge of the constituent datasets yields poor performance due to inconsistent taxonomies and annotation practices. We reconcile the taxonomies and bring the pixel-level annotations into alignment by relabeling more than 220,000 object masks in more than 80,000 images. The resulting composite dataset enables training a single semantic segmentation model that functions effectively across domains and generalizes to datasets that were not seen during training. We adopt zero-shot cross-dataset transfer as a benchmark to systematically evaluate a model's robustness and show that MSeg training yields substantially more robust models in comparison to training on individual datasets or naive mixing of datasets without the presented contributions. A model trained on MSeg ranks first on the WildDash leaderboard for robust semantic segmentation, with no exposure to WildDash data during training.

Citation

Please use the following citation when referencing the dataset:

@InProceedings{MSeg_2020_CVPR,
author = {Lambert, John and Zhuang, Liu and Sener, Ozan and Hays, James and Koltun, Vladlen},
title = {{MSeg}: A Composite Dataset for Multi-domain Semantic Segmentation},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
year = {2020}
}
数据集反馈
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