The Cityscapes Dataset for Semantic Urban Scene Understanding
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...To do this, we initialize the corresponding 7 categories in Cityscapes from a pre-trained COCO Mask R-CNN model (rider being randomly initialized)....
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...Experiments on Cityscapes We further report instance segmentation results on the Cityscapes [7] dataset....
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...For the person and car categories, the Cityscapes dataset exhibits a large number of within-category overlapping instances (on average 6 people and 9 cars per image)....
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...A main challenge of the Cityscapes dataset is training models in a low-data regime, particularly for the categories of truck, bus, and train, which have about 200-500 train- ing samples each....
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...We further report instance segmentation results on the Cityscapes [7] dataset....
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11,958 citations
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...• Semantic labels↔photo, trained on the Cityscapes dataset [11]....
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...(2) Accuracy: we obtain state-of-art results on several challenging datasets, including the PASCAL VOC 2012 semantic segmentation benchmark [34], PASCAL-Context [35], PASCALPerson-Part [36], and Cityscapes [37]....
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...Dataset: Cityscapes [37] is a recently released large-scale dataset, which contains high quality pixel-level annotations of 5000 images collected in street scenes from 50 different cities....
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...We evaluate the proposed models on four challenging datasets: PASCAL VOC 2012, PASCAL-Context, PASCALPerson-Part, and Cityscapes....
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...Cityscapes [37] is a recently released large-scale dataset, which contains high quality pixel-level annotations of 5,000 images collected in street scenes from 50...
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...benchmark [34], PASCAL-Context [35], PASCAL-PersonPart [36], and Cityscapes [37]....
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11,682 citations
References
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"The Cityscapes Dataset for Semantic..." refers background or methods in this paper
...These new approaches take enormous advantage from recently introduced large-scale datasets, e.g. PASCALContext [45] and Microsoft COCO [38]....
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...A major contributing factor to their success is the availability of large-scale, publicly available datasets such as ImageNet [59], PASCAL VOC [14], PASCAL-Context [45], and Microsoft COCO [38] that allow deep neural networks to develop their full potential....
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...Specifically, we follow [38] and use 10 different overlaps ranging from 0.5 to 0.95 in steps of 0.05....
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...We attribute this to both our higher-resolution imagery and the careful annotation procedure....
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