Fetching the paper…

Reading the bibliography…

2019

Semantic Segmentation of Video Sequences with Convolutional LSTMs

Pfeuffer, Andreas, Schulz, Karina, Dietmayer, Klaus

Original
PDF
Post on X

Understand

Most of the semantic segmentation approaches have been developed for single image segmentation, and hence, video sequences are currently segmented by processing each frame of the video sequence separately.

The disadvantage of this is that temporal image information is not considered, which improves the performance of the segmentation approach.
One possibility to include temporal information is to use recurrent neural networks.
However, there are only a few approaches using recurrent networks for video segmentation so far.

Semantic Segmentation of Video Sequences with Convolutional LSTMs · Around

Built on

Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Generating sequences with recurrent neural networks
Original
Alex Graves · 2013
Earlier work this paper cites.
Large-scale video classification with convolutional neural networks
A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Original
Karen Simonyan and Andrew Zisserman · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Original
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
Earlier work this paper cites.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Martín Abadi, Ashish Agarwal, Paul Barham, and Eugene Brevdo et al · 2015
Earlier work this paper cites.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Original
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla · 2015
Earlier work this paper cites.
Scene labeling with lstm recurrent neural networks
W. Byeon, T. M. Breuel, F. Raue, and M. Liwicki · 2015
Earlier work this paper cites.

Similar

Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Original
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Original
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Original
Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele · 2016
Cited alongside, same era.
Virtual worlds as proxy for multi-object tracking analysis
A Gaidon, Q Wang, Y Cabon, and E Vig · 2016
Cited alongside, same era.
RGB-D scene labeling with long short-term memorized fusion model

Then

Fully convolutional networks for semantic segmentation
Original
Evan Shelhamer, Jonathan Long, and Trevor Darrell · 2016
Later among the works it cites.
Aggregated residual transformations for deep neural networks
Original
Saining Xie, Ross B. Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
Later among the works it cites.
Pyramid scene parsing network
Original
Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2016
Later among the works it cites.
Recurrent fully convolutional networks for video segmentation
Sepehr Valipour, Mennatullah Siam, Martin Jägersand, and Nilanjan Ray · 2017
Later among the works it cites.
Semantic segmentation of rgbd videos with recurrent fully convolutional neural networks
E. E. Yurdakul and Y. Yemez · 2017

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

Zhen Li, Yukang Gan, Xiaodan Liang, Yizhou Yu, Hui Cheng, and Liang Lin · 2016

Cited alongside, same era.

Semantic object parsing with graph LSTM

Xiaodan Liang, Xiaohui Shen, Jiashi Feng, Liang Lin, and Shuicheng Yan · 2016

Cited alongside, same era.

Enet: A deep neural network architecture for real-time semantic segmentation

Adam Paszke, Abhishek Chaurasia, Sangpil Kim, and Eugenio Culurciello · 2016

Cited alongside, same era.

End-to-end instance segmentation and counting with recurrent attention

Mengye Ren and Richard S. Zemel · 2016

Cited alongside, same era.

Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling

Juergen T. Geiger, Zixing Zhang, Felix Weninger, Björn Schuller, and Gerhard Rigoll

Cited in the paper.

Deep convolutional and lstm neural networks for acoustic modelling in automatic speech recognition

Xiaoyu Liu

Cited in the paper.

Later among the works it cites.

Icnet for real-time semantic segmentation on high-resolution images

Hengshuang Zhao, Xiaojuan Qi, Xiaoyong Shen, Jianping Shi, and Jiaya Jia · 2017

Later among the works it cites.

Encoder-decoder with atrous separable convolution for semantic image segmentation

Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam · 2018

Later among the works it cites.

Future semantic segmentation with convolutional lstm, 07 2018

Seyed shahabeddin Nabavi, Mrigank Rochan, Yang , and Wang · 2018

Later among the works it cites.