# Dynamic Video Segmentation Network

arXiv: https://arxiv.org/abs/1804.00931 · HTML: https://mlsanity.com/paper/1804.00931/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 30 of 51)

- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Efficient Regional Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2103.12934/) (2021)
- [Temporal Memory Attention for Video Semantic Segmentation](https://mlsanity.com/paper/2102.08643/) (2021)
- [Per-Clip Video Object Segmentation](https://mlsanity.com/paper/2208.01924/) (2022)
- [Reliable Propagation-Correction Modulation for Video Object Segmentation](https://mlsanity.com/paper/2112.02853/) (2021)
- [Learning Quality-aware Dynamic Memory for Video Object Segmentation](https://mlsanity.com/paper/2207.07922/) (2022)
- [Time Does Tell: Self-Supervised Time-Tuning of Dense Image Representations](https://mlsanity.com/paper/2308.11796/) (2023)
- [Look Before You Match: Instance Understanding Matters in Video Object Segmentation](https://mlsanity.com/paper/2212.06826/) (2022)
- [Mask Propagation for Efficient Video Semantic Segmentation](https://mlsanity.com/paper/2310.18954/) (2023)
- [Local Memory Attention for Fast Video Semantic Segmentation](https://mlsanity.com/paper/2101.01715/) (2021)
- [Mining Relations among Cross-Frame Affinities for Video Semantic Segmentation](https://mlsanity.com/paper/2207.10436/) (2022)
- [Learning Local and Global Temporal Contexts for Video Semantic Segmentation](https://mlsanity.com/paper/2204.03330/) (2022)
- [Global Spectral Filter Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2210.05567/) (2022)
- [Domain Adaptive Video Segmentation via Temporal Consistency Regularization](https://mlsanity.com/paper/2107.11004/) (2021)
- [Generative Adversarial Network for Future Hand Segmentation from Egocentric Video](https://mlsanity.com/paper/2203.11305/) (2022)
- [MobileVOS: Real-Time Video Object Segmentation Contrastive Learning meets Knowledge Distillation](https://mlsanity.com/paper/2303.07815/) (2023)
- [Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving](https://mlsanity.com/paper/2312.05385/) (2023)
- [BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation](https://mlsanity.com/paper/2208.01159/) (2022)
- [Event-guided Low-light Video Semantic Segmentation](https://mlsanity.com/paper/2411.00639/) (2024)
- [Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes](https://mlsanity.com/paper/2401.15261/) (2024)
- [GSVNet: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video](https://mlsanity.com/paper/2103.08834/) (2021)
- [RankSeg: Adaptive Pixel Classification with Image Category Ranking for Segmentation](https://mlsanity.com/paper/2203.04187/) (2022)
- [Towards Robust Video Object Segmentation with Adaptive Object Calibration](https://mlsanity.com/paper/2207.00887/) (2022)
- [Unsupervised Domain Adaptation for Video Semantic Segmentation](https://mlsanity.com/paper/2107.11052/) (2021)
- [BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies](https://mlsanity.com/paper/2108.09376/) (2021)
- [Efficient Semantic Segmentation by Altering Resolutions for Compressed Videos](https://mlsanity.com/paper/2303.07224/) (2023)
- [AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions](https://mlsanity.com/paper/2305.03387/) (2023)
- [On Efficient Real-Time Semantic Segmentation: A Survey](https://mlsanity.com/paper/2206.08605/) (2022)
- [Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation](https://mlsanity.com/paper/2309.08020/) (2023)

## Abstract (arXiv)

In this paper, we present a detailed design of dynamic video segmentation network (DVSNet) for fast and efficient semantic video segmentation. DVSNet consists of two convolutional neural networks: a segmentation network and a flow network. The former generates highly accurate semantic segmentations, but is deeper and slower. The latter is much faster than the former, but its output requires further processing to generate less accurate semantic segmentations. We explore the use of a decision network to adaptively assign different frame regions to different networks based on a metric called expected confidence score. Frame regions with a higher expected confidence score traverse the flow network. Frame regions with a lower expected confidence score have to pass through the segmentation network. We have extensively performed experiments on various configurations of DVSNet, and investigated a number of variants for the proposed decision network. The experimental results show that our DVSNet is able to achieve up to 70.4% mIoU at 19.8 fps on the Cityscape dataset. A high speed version of DVSNet is able to deliver an fps of 30.4 with 63.2% mIoU on the same dataset. DVSNet is also able to reduce up to 95% of the computational workloads.