scispace - formally typeset
Open AccessProceedings ArticleDOI

The Cityscapes Dataset for Semantic Urban Scene Understanding

TLDR
This work introduces Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling, and exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity.
Abstract
Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

read more

Citations
More filters
Journal ArticleDOI

Semantic Segmentation for High Spatial Resolution Remote Sensing Images Based on Convolution Neural Network and Pyramid Pooling Module

TL;DR: An end-to-end framework to semantically segment high-resolution aerial images without postprocessing to refine the segmentation results is proposed and the results validate the efficiency of the proposed model in segmenting multiple ground objects from remotely sensed images simultaneously.
Posted Content

High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks

TL;DR: A method for high-performance semantic image segmentation based on very deep residual networks, which achieves the state-of-the-art performance and demonstrates that online bootstrapping is critically important for achieving good accuracy.
Proceedings ArticleDOI

Stochastic Classifiers for Unsupervised Domain Adaptation

TL;DR: This paper introduces a novel method called STochastic clAssifieRs (STAR) for addressing the problem of misaligned local regions between source and target domain, which finds that using more classifiers leads to better performance, but also introduces more model parameters, therefore risking overfitting.
Proceedings ArticleDOI

Towards Real-Time Unsupervised Monocular Depth Estimation on CPU

TL;DR: In this article, a pyramid of features extracted from a single input image is used for unsupervised monocular depth estimation on a CPU, even of an embedded system, using a pyramid-based CNN.
Journal ArticleDOI

Cross-View Semantic Segmentation for Sensing Surroundings

TL;DR: A novel visual task called Cross-view Semantic Segmentation as well as a framework named View Parsing Network (VPN) to address it and the experimental results show that the model can effectively make use of the information from different views and multi-modalities to understanding spatial information.
References
More filters
Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI

ImageNet Large Scale Visual Recognition Challenge

TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Related Papers (5)
Trending Questions (1)
What is city scene understanding?

City scene understanding involves pixel-level and instance-level semantic labeling in urban environments. The Cityscapes dataset provides a benchmark for training and testing approaches in this area.