Fully convolutional networks for semantic segmentation
Jonathan Long,Evan Shelhamer,Trevor Darrell +2 more
- pp 3431-3440
TLDR
The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.Abstract:
Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.read more
Citations
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Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks
Michael Wurm,Thomas Stark,Xiao Xiang Zhu,Xiao Xiang Zhu,Matthias Weigand,Matthias Weigand,Hannes Taubenböck +6 more
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A Survey on Vision Transformer
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Feature Space Optimization for Semantic Video Segmentation
TL;DR: An approach to long-range spatio-temporal regularization in semantic video segmentation by optimizing the mapping of pixels to a Euclidean feature space so as to minimize distances between corresponding points.
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A Two-Streamed Network for Estimating Fine-Scaled Depth Maps from Single RGB Images
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TL;DR: In this article, a fast-to-train two-streamed CNN is proposed to predict depth and depth gradients, which are then fused together into an accurate and detailed depth map.
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