# Exploiting Temporality for Semi-Supervised Video Segmentation

arXiv: https://arxiv.org/abs/1908.11309 · HTML: https://mlsanity.com/paper/1908.11309/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2019.

## Built on by (most cited first, 7 of 7)

- [AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic Segmentation](https://mlsanity.com/paper/2110.12369/) (2021)
- [Perceptual Consistency in Video Segmentation](https://mlsanity.com/paper/2110.12385/) (2021)
- [Delta Distillation for Efficient Video Processing](https://mlsanity.com/paper/2203.09594/) (2022)
- [Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation](https://mlsanity.com/paper/2112.13815/) (2021)
- [CoVA: Exploiting Compressed-Domain Analysis to Accelerate Video Analytics](https://mlsanity.com/paper/2207.00588/) (2022)
- [Deep Common Feature Mining for Efficient Video Semantic Segmentation](https://mlsanity.com/paper/2403.02689/) (2024)
- [Stereo Video Reconstruction Without Explicit Depth Maps for Endoscopic Surgery](https://mlsanity.com/paper/2109.08227/) (2021)

## Abstract (arXiv)

In recent years, there has been remarkable progress in supervised image segmentation. Video segmentation is less explored, despite the temporal dimension being highly informative. Semantic labels, e.g. that cannot be accurately detected in the current frame, may be inferred by incorporating information from previous frames. However, video segmentation is challenging due to the amount of data that needs to be processed and, more importantly, the cost involved in obtaining ground truth annotations for each frame. In this paper, we tackle the issue of label scarcity by using consecutive frames of a video, where only one frame is annotated. We propose a deep, end-to-end trainable model which leverages temporal information in order to make use of easy to acquire unlabeled data. Our network architecture relies on a novel interconnection of two components: a fully convolutional network to model spatial information and temporal units that are employed at intermediate levels of the convolutional network in order to propagate information through time. The main contribution of this work is the guidance of the temporal signal through the network. We show that only placing a temporal module between the encoder and decoder is suboptimal (baseline). Our extensive experiments on the CityScapes dataset indicate that the resulting model can leverage unlabeled temporal frames and significantly outperform both the frame-by-frame image segmentation and the baseline approach.