Citations
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Proceedings ArticleDOI
Pyramid Scene Parsing Network
TL;DR: This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.
Posted Content
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
TL;DR: This work considers image transformation problems, and proposes the use of perceptual loss functions for training feed-forward networks for image transformation tasks, and shows results on image style transfer, where aFeed-forward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.
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Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu,Vladlen Koltun +1 more
TL;DR: In this article, a new convolutional network module is proposed to aggregate multi-scale contextual information without losing resolution, and the architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage.
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Learning Deconvolution Network for Semantic Segmentation
TL;DR: In this paper, a deconvolution network is proposed to identify pixel-wise class labels and predict segmentation masks in an input image, and construct the final semantic segmentation map by combining the results from all proposals.
Proceedings ArticleDOI
Scene Parsing through ADE20K Dataset
TL;DR: The ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, is introduced and it is shown that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.
References
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Proceedings ArticleDOI
Pyramid Scene Parsing Network
TL;DR: This paper exploits the capability of global context information by different-region-based context aggregation through the pyramid pooling module together with the proposed pyramid scene parsing network (PSPNet) to produce good quality results on the scene parsing task.
Book ChapterDOI
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
TL;DR: In this paper, the authors combine the benefits of both approaches, and propose the use of perceptual loss functions for training feed-forward networks for image style transfer, where a feedforward network is trained to solve the optimization problem proposed by Gatys et al. in real-time.
Posted Content
Learning Deconvolution Network for Semantic Segmentation
TL;DR: In this paper, a deconvolution network is proposed to identify pixel-wise class labels and predict segmentation masks in an input image, and construct the final semantic segmentation map by combining the results from all proposals.
Proceedings ArticleDOI
Scene Parsing through ADE20K Dataset
TL;DR: The ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, is introduced and it is shown that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis.
Posted Content
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
TL;DR: A novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation, which is up to 18 times faster, requires 75% less FLOPs, has 79% less parameters, and provides similar or better accuracy to existing models.
Related Papers (5)
A Review of Semantic Segmentation Based on Context Information
Wei Xu,Lingjun Yang,Sanxing Cao +2 more