The Cityscapes Dataset for Semantic Urban Scene Understanding
Marius Cordts,Mohamed Omran,Sebastian Ramos,Timo Rehfeld,Markus Enzweiler,Rodrigo Benenson,Uwe Franke,Stefan Roth,Bernt Schiele +8 more
- pp 3213-3223
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
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SCAttNet: Semantic Segmentation Network With Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images
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Context-Based Path Prediction for Targets with Switching Dynamics
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Exploring Cross-Image Pixel Contrast for Semantic Segmentation
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Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing
TL;DR: A new large-scale database "Multi-Human Parsing (MHP)" is presented for algorithm development and evaluation, and NAN consistently outperforms existing state-of-the-art solutions on the MHP and several other datasets, and serves as a strong baseline to drive the future research for multi-human parsing.
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Scaling Out-of-Distribution Detection for Real-World Settings
Dan Hendrycks,Steven Basart,Mantas Mazeika,Mohammadreza Mostajabi,Jacob Steinhardt,Dawn Song +5 more
TL;DR: This work departs from small-scale settings and explores large-scale multiclass and multi-label settings with high-resolution images and hundreds of classes for out-of-distribution detection, finding that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large- scale multi-class, multi- label, and segmentation tasks.
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