# Semantic Segmentation of Video Sequences with Convolutional LSTMs

arXiv: https://arxiv.org/abs/1905.01058 · HTML: https://mlsanity.com/paper/1905.01058/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

## Built on by (most cited first, 14 of 14)

- [Robust High-Resolution Video Matting with Temporal Guidance](https://mlsanity.com/paper/2108.11515/) (2021)
- [Sequential vessel segmentation via deep channel attention network](https://mlsanity.com/paper/2102.05229/) (2021)
- [Exploiting Temporal State Space Sharing for Video Semantic Segmentation](https://mlsanity.com/paper/2503.20824/) (2025)
- [A recurrent CNN for online object detection on raw radar frames](https://mlsanity.com/paper/2212.11172/) (2022)
- [Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture](https://mlsanity.com/paper/2106.10118/) (2021)
- [Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation](https://mlsanity.com/paper/2112.13815/) (2021)
- [Bootstrapped Self-Supervised Training with Monocular Video for Semantic Segmentation and Depth Estimation](https://mlsanity.com/paper/2103.11031/) (2021)
- [A High Resolution Urban and Rural Settlement Map of Africa Using Deep Learning and Satellite Imagery](https://mlsanity.com/paper/2411.02935/) (2024)
- [On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models](https://mlsanity.com/paper/2110.03054/) (2021)
- [Detecting Slag Formations with Deep Convolutional Neural Networks](https://mlsanity.com/paper/2110.06640/) (2021)
- [MCDS-VSS: Moving Camera Dynamic Scene Video Semantic Segmentation by Filtering with Self-Supervised Geometry and Motion](https://mlsanity.com/paper/2405.19921/) (2024)
- [M-Net: MRI Brain Tumor Sequential Segmentation Network via Mesh-Cast](https://mlsanity.com/paper/2507.20582/) (2025)
- [RS-SSM: Refining Forgotten Specifics in State Space Model for Video Semantic Segmentation](https://mlsanity.com/paper/2603.24295/) (2026)
- [Shift-Memory Network for Temporal Scene Segmentation](https://mlsanity.com/paper/2202.08399/) (2022)

## Abstract (arXiv)

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. These approaches extend the encoder-decoder network architecture of well-known segmentation approaches and place convolutional LSTM layers between encoder and decoder. However, in this paper it is shown that this position is not optimal, and that other positions in the network exhibit better performance. Nowadays, state-of-the-art segmentation approaches rarely use the classical encoder-decoder structure, but use multi-branch architectures. These architectures are more complex, and hence, it is more difficult to place the recurrent units at a proper position. In this work, the multi-branch architectures are extended by convolutional LSTM layers at different positions and evaluated on two different datasets in order to find the best one. It turned out that the proposed approach outperforms the pure CNN-based approach for up to 1.6 percent.