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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References
More filters
Book ChapterDOI
Superparsing: scalable nonparametric image parsing with superpixels
Joseph Tighe,Svetlana Lazebnik +1 more
TL;DR: This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in this case, superpixels produced by bottom-up segmentation) with their categories, and establishes a new benchmark for the problem.
Book ChapterDOI
Semantic segmentation with second-order pooling
TL;DR: This paper introduces multiplicative second-order analogues of average and max-pooling that together with appropriate non-linearities lead to state-of-the-art performance on free-form region recognition, without any type of feature coding.
Proceedings ArticleDOI
Restoring an Image Taken through a Window Covered with Dirt or Rain
TL;DR: This work presents a post-capture image processing solution that can remove localized rain and dirt artifacts from a single image, and demonstrates effective removal of dirt and rain in outdoor test conditions.
Proceedings ArticleDOI
Convolutional feature masking for joint object and stuff segmentation
Jifeng Dai,Kaiming He,Jian Sun +2 more
TL;DR: Wang et al. as mentioned in this paper proposed to exploit shape information via masking convolutional features, where the proposal segments (e.g., super-pixels) are treated as masks on the CNN feature maps and used to train classifiers for recognition.
Journal ArticleDOI
Toward automatic phenotyping of developing embryos from videos
TL;DR: A trainable system for analyzing videos of developing C. elegans embryos that automatically detects, segments, and locates cells and nuclei in microscopic images and contains a set of elastic models of the embryo at various stages of development that are matched to the label images.