15 datasets found

Semantic segmentation is a critical computer vision component of autonomous navigation and robotic systems. It involves dense and high dimensional prediction of a label for...

This paper addresses fast semantic segmentation on video. The dataset used is Cityscapes and CamVid.

The Synthia→Cityscapes task is a domain adaptation task for semantic segmentation, where the source domain is Synthia and the target domain is Cityscapes.

The Pascal VOC, Pascal Context, COCO-Object, Cityscapes, and ADE20k datasets are used for evaluation of the proposed method.

The Camvid and Cityscapes datasets are used for semantic segmentation tasks.

State-of-the-art techniques in weakly-supervised semantic segmentation (WSSS) using image-level labels exhibit severe performance degradation on driving scene datasets such as...

Cityscapes dataset is a large-scale urban scene dataset containing 30,000 images of street scenes.

The dataset used in this paper is ImageNet and CIFAR-10 for image classification, and Cityscapes for semantic segmentation.

The Cityscapes dataset is a large-scale urban scene dataset containing over 25,000 images.

The dataset used in the paper is MS COCO, Cityscapes, and CTW1500. The authors used these datasets for instance segmentation and few-shot instance segmentation tasks.

The Cityscapes dataset consists of 8 thing classes and 11 stuff classes.

The SYNTHIA dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

The GTA5 dataset is a synthetic dataset for semantic segmentation, and the Cityscapes dataset is a real-world dataset for semantic segmentation.

The GTA5→Cityscapes dataset is a synthetic-to-real benchmark dataset for domain adaptation in semantic segmentation.

The Cityscapes dataset is a large and famous city street scene semantic segmentation dataset. 19 classes of which 30 classes of this dataset are considered for training and...