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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DSEC: A Stereo Event Camera Dataset for Driving Scenarios
TL;DR: The DSEC dataset as mentioned in this paper is a large-scale stereo dataset with event cameras, which contains 53 sequences collected by driving in a variety of illumination conditions and provides ground truth disparity for the development and evaluation of event-based stereo algorithms.
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Testing machine learning based systems: a systematic mapping
Vincenzo Riccio,Gunel Jahangirova,Andrea Stocco,Nargiz Humbatova,Michael Weiss,Paolo Tonella +5 more
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ViewAL: Active Learning With Viewpoint Entropy for Semantic Segmentation
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Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
TL;DR: Zhang et al. as mentioned in this paper proposed a coarse-to-fine feature adaptation approach to cross-domain object detection, where foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space.
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Recurrent Scene Parsing with Perspective Understanding in the Loop
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