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
More filters
Journal ArticleDOI
Single Image Dehazing with a Generic Model-Agnostic Convolutional Neural Network
TL;DR: A simple convolutional neural network is proposed in this letter and is trained end-to-end to restore clear images from hazy inputs and achieves record-breaking dehazing performance on several standard data sets that are synthesized using the atmosphere scattering model.
Proceedings Article
FasterSeg: Searching for Faster Real-time Semantic Segmentation
TL;DR: This work presents FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods, and proposes a decoupled and fine-grained latency regularization that effectively overcomes the observed phenomenons that the searched networks are prone to "collapsing" to low-latency yet poor-accuracy models.
Proceedings ArticleDOI
Masked-attention Mask Transformer for Universal Image Segmentation
TL;DR: Mask2former as discussed by the authors proposes Masked-Attention Mask Transformer (Mask2Transformer), which extracts localized features by constraining cross-attention within predicted mask regions. But it is not suitable for instance segmentation.
Proceedings ArticleDOI
RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features
TL;DR: RefineMask as mentioned in this paper incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner, fusing more detailed information stage by stage, which is able to refine high-quality masks consistently.
Journal ArticleDOI
Pixel and feature level based domain adaptation for object detection in autonomous driving
TL;DR: Zhang et al. as mentioned in this paper proposed a novel unsupervised domain adaptation (UDA) model which integrates both image and feature level based adaptations to solve the cross-domain object detection problem.
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
Karen Simonyan,Andrew Zisserman +1 more
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
Karen Simonyan,Andrew Zisserman +1 more
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
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
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.