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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Self-supervised Domain Adaptation for Computer Vision Tasks
TL;DR: Li et al. as mentioned in this paper proposed a self-supervised domain adaptation method using object recognition and semantic segmentation of urban scenes as use cases, focusing on simple pretext/auxiliary tasks (e.g. image rotation prediction).
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VSO: Visual Semantic Odometry
TL;DR: A novel visual semantic odometry (VSO) framework to enable medium-term continuous tracking of points using semantics and demonstrates a significant improvement over state-of-the-art baselines in the context of autonomous driving simply by integrating the authors' semantic constraints.
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Bidirectional Learning for Domain Adaptation of Semantic Segmentation
TL;DR: In this paper, a bidirectional learning framework was proposed for domain adaptation of semantic image segmentation, where the image translation model and the segmentation adaptation model can be learned alternatively and promote to each other.
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Urban Land Use and Land Cover Classification Using Novel Deep Learning Models Based on High Spatial Resolution Satellite Imagery.
TL;DR: These novel multi-scale deep learning models outperformed the state-of-the-art models, e.g., U-Net, convolutional neural network (CNN) and Support Vector Machine (SVM) model over both WV2 and WV3 images, and yielded robust and efficient urban land cover classification results.
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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
TL;DR: In this article, a Curriculum Model Adaptation (CMAda) method is proposed to gradually adapt a semantic segmentation model from light synthetic fog to dense real fog in multiple steps, using both synthetic and real foggy data.
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