# Video Semantic Segmentation with Distortion-Aware Feature Correction

arXiv: https://arxiv.org/abs/2006.10380 · HTML: https://mlsanity.com/paper/2006.10380/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [Delivering Arbitrary-Modal Semantic Segmentation](https://mlsanity.com/paper/2303.01480/) (2023)
- [Deep Common Feature Mining for Efficient Video Semantic Segmentation](https://mlsanity.com/paper/2403.02689/) (2024)
- [Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature Refinement](https://mlsanity.com/paper/2301.03832/) (2023)
- [A Threefold Review on Deep Semantic Segmentation: Efficiency-oriented, Temporal and Depth-aware design](https://mlsanity.com/paper/2303.04315/) (2023)
- [OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation](https://mlsanity.com/paper/2307.15588/) (2023)
- [EAVL: Explicitly Align Vision and Language for Referring Image Segmentation](https://mlsanity.com/paper/2308.09779/) (2023)
- [Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation](https://mlsanity.com/paper/2206.09604/) (2022)
- [Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2309.14786/) (2023)
- [Multimodal Fusion of RGB and Complementary Modalities for Semantic Segmentation](https://mlsanity.com/paper/2610.17800/) (2026)

## Abstract (arXiv)

Video semantic segmentation is active in recent years benefited from the great progress of image semantic segmentation. For such a task, the per-frame image segmentation is generally unacceptable in practice due to high computation cost. To tackle this issue, many works use the flow-based feature propagation to reuse the features of previous frames. However, the optical flow estimation inevitably suffers inaccuracy and then causes the propagated features distorted. In this paper, we propose distortion-aware feature correction to alleviate the issue, which improves video segmentation performance by correcting distorted propagated features. To be specific, we firstly propose to transfer distortion patterns from feature into image space and conduct effective distortion map prediction. Benefited from the guidance of distortion maps, we proposed Feature Correction Module (FCM) to rectify propagated features in the distorted areas. Our proposed method can significantly boost the accuracy of video semantic segmentation at a low price. The extensive experimental results on Cityscapes and CamVid show that our method outperforms the recent state-of-the-art methods.