Understanding Convolution for Semantic Segmentation

@article{Wang2018UnderstandingCF,
  title={Understanding Convolution for Semantic Segmentation},
  author={Panqu Wang and Pengfei Chen and Ye Yuan and Ding Liu and Zehua Huang and Xiaodi Hou and G. Cottrell},
  journal={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2018},
  pages={1451-1460}
}
Recent advances in deep learning, especially deep convolutional neural networks (CNNs), have led to significant improvement over previous semantic segmentation systems. [...] Key Method First, we design dense upsampling convolution (DUC) to generate pixel-level prediction, which is able to capture and decode more detailed information that is generally missing in bilinear upsampling. Second, we propose a hybrid dilated convolution (HDC) framework in the encoding phase.Expand
Multilevel feature fusion dilated convolutional network for semantic segmentation
Recently, convolutional neural network (CNN) has led to significant improvement in the field of computer vision, especially the improvement of the accuracy and speed of semantic segmentation tasks,Expand
ESNet: An Efficient Symmetric Network for Real-time Semantic Segmentation
TLDR
This paper designs an efficient symmetric network, called (ESNet), to address the problem of real-time semantic segmentation on CityScapes dataset, and achieves state-of-the-art results in terms of speed and accuracy trade-off. Expand
RelationNet: Learning Deep-Aligned Representation for Semantic Image Segmentation
TLDR
A novel deep neural network named Relation net is proposed, which utilizes CNN and RNN to aggregate context information and a spatial correlation loss is applied to train RelationNet to align features of spatial pixels belonging to same category. Expand
Dense Convolutional Networks for Semantic Segmentation
TLDR
The dense convolution unit (DCU) is proposed, which is more suitable for pixel-wise classification and serves as the classification layer and is a better option than standard convolution in FCNs. Expand
Stacked Deconvolutional Network for Semantic Segmentation
TLDR
This work proposes a Stacked Deconvolutional Network (SDN) for semantic segmentation and achieves the new state-ofthe- art results on four datasets, including PASCAL VOC 2012, CamVid, GATECH, COCO Stuff. Expand
Optimized HRNet for image semantic segmentation
TLDR
An optimized high-resolution net (HRNet) for image semantic segmentation is proposed and a mixed dilated convolution (MDC) module is introduced, which can not only increase the diversity of the receptive fields, but also tackle the “gridding” problem commonly existing in the conventional dilated Convolution. Expand
Vortex Pooling: Improving Context Representation in Semantic Segmentation
TLDR
This paper argues that, when predicting the category of a given pixel, the regions close to the target are more important than those far from it, and proposes an effective yet efficient approach named Vortex Pooling to effectively utilize contextual information. Expand
Multi-Receptive Atrous Convolutional Network for Semantic Segmentation
TLDR
This paper adapts the ResNet-101 model as the backbone network and proposes a MRACN segmentation model (MRACN-Seg), which captures the multi-receptive features and the global features at different receptive scales of the input. Expand
Rethinking Atrous Convolution for Semantic Image Segmentation
TLDR
The proposed `DeepLabv3' system significantly improves over the previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark. Expand
Enlarging Effective Receptive Field of Convolutional Neural Networks for Better Semantic Segmentation
TLDR
This paper proposes a novel deep neural network module, namely group dilated convolution(GDC), to effectively enlarge the receptive field, and a top-to-down pathway network is exploited simultaneously. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 54 REFERENCES
High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks
TLDR
A method for high-performance semantic image segmentation based on very deep residual networks, which achieves the state-of-the-art performance and demonstrates that online bootstrapping is critically important for achieving good accuracy. Expand
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
TLDR
This work addresses the task of semantic image segmentation with Deep Learning and proposes atrous spatial pyramid pooling (ASPP), which is proposed to robustly segment objects at multiple scales, and improves the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. Expand
Fully Convolutional Networks for Semantic Segmentation
TLDR
It is shown that convolutional networks by themselves, trained end- to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. Expand
Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation
TLDR
This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Expand
Learning Deconvolution Network for Semantic Segmentation
TLDR
A novel semantic segmentation algorithm by learning a deep deconvolution network on top of the convolutional layers adopted from VGG 16-layer net, which demonstrates outstanding performance in PASCAL VOC 2012 dataset. Expand
Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
TLDR
This work brings together methods from DCNNs and probabilistic graphical models for addressing the task of pixel-level classification by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF). Expand
Pushing the Boundaries of Boundary Detection using Deep Learning
TLDR
This work shows that adapting Deep Convolutional Neural Network training to the task of boundary detection can result in substantial improvements over the current state-of-the-art in boundary detection, and examines the potential of the boundary detector in conjunction with thetask of semantic segmentation. Expand
Higher Order Conditional Random Fields in Deep Neural Networks
TLDR
Two types of higher order potentials, based on object detections and superpixels, can be included in a CRF embedded within a deep network to allow inference with the differentiable mean field algorithm and are designed to achieve state-of-the-art segmentation performance on the PASCAL VOC benchmark. Expand
Multi-Scale Context Aggregation by Dilated Convolutions
TLDR
This work develops a new convolutional network module that is specifically designed for dense prediction, and shows that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. Expand
Semantic Image Segmentation via Deep Parsing Network
TLDR
This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts by proposing a Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which enables deterministic end-to-end computation in a single forward pass. Expand
...
1
2
3
4
5
...