# Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation

arXiv: https://arxiv.org/abs/2012.11655 · HTML: https://mlsanity.com/paper/2012.11655/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2020.

## Built on by (most cited first, 28 of 28)

- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [LVOS: A Benchmark for Long-term Video Object Segmentation](https://mlsanity.com/paper/2211.10181/) (2022)
- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [XMem++: Production-level Video Segmentation From Few Annotated Frames](https://mlsanity.com/paper/2307.15958/) (2023)
- [Recurrent Dynamic Embedding for Video Object Segmentation](https://mlsanity.com/paper/2205.03761/) (2022)
- [A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection](https://mlsanity.com/paper/2203.04708/) (2022)
- [Language-Bridged Spatial-Temporal Interaction for Referring Video Object Segmentation](https://mlsanity.com/paper/2206.03789/) (2022)
- [LVOS: A Benchmark for Large-scale Long-term Video Object Segmentation](https://mlsanity.com/paper/2404.19326/) (2024)
- [Learning Quality-aware Dynamic Memory for Video Object Segmentation](https://mlsanity.com/paper/2207.07922/) (2022)
- [Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation](https://mlsanity.com/paper/2403.12042/) (2024)
- [Global Spectral Filter Memory Network for Video Object Segmentation](https://mlsanity.com/paper/2210.05567/) (2022)
- [Boosting Video Object Segmentation via Space-time Correspondence Learning](https://mlsanity.com/paper/2304.06211/) (2023)
- [EdgeTAM: On-Device Track Anything Model](https://mlsanity.com/paper/2501.07256/) (2025)
- [Region Aware Video Object Segmentation with Deep Motion Modeling](https://mlsanity.com/paper/2207.10258/) (2022)
- [Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview](https://mlsanity.com/paper/2211.08352/) (2022)
- [SimulFlow: Simultaneously Extracting Feature and Identifying Target for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2311.18286/) (2023)
- [Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection](https://mlsanity.com/paper/2403.01968/) (2024)
- [Adversarial Attacks on Video Object Segmentation with Hard Region Discovery](https://mlsanity.com/paper/2309.13857/) (2023)
- [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://mlsanity.com/paper/2501.07451/) (2025)
- [FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow](https://mlsanity.com/paper/2111.10531/) (2021)
- [Efficient Video Object Segmentation via Modulated Cross-Attention Memory](https://mlsanity.com/paper/2403.17937/) (2024)
- [Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels](https://mlsanity.com/paper/2203.16678/) (2022)
- [UniST: Towards Unifying Saliency Transformer for Video Saliency Prediction and Detection](https://mlsanity.com/paper/2309.08220/) (2023)
- [CamoSAM2: SAM2-oriented Prompt Auto-Refinement for Video Camouflaged Object Detection](https://mlsanity.com/paper/2504.00375/) (2025)
- [Depth Quality-Inspired Feature Manipulation for Efficient RGB-D and Video Salient Object Detection](https://mlsanity.com/paper/2208.03918/) (2022)
- [Video Object of Interest Segmentation](https://mlsanity.com/paper/2212.02871/) (2022)

## Abstract (arXiv)

Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame. This results in high-quality segmentation across challenging scenarios such as changes in appearance and occlusion. But it also leads to unnecessary computations for stationary or slow-moving objects where the change across frames is minimal. In this work, we exploit this observation by using temporal information to quickly identify frames with minimal change and skip the heavyweight mask generation step. To realize this efficiency, we propose a novel dynamic network that estimates change across frames and decides which path -- computing a full network or reusing previous frame's feature -- to choose depending on the expected similarity. Experimental results show that our approach significantly improves inference speed without much accuracy degradation on challenging Semi-VOS datasets -- DAVIS 16, DAVIS 17, and YouTube-VOS. Furthermore, our approach can be applied to multiple Semi-VOS methods demonstrating its generality. The code is available in https://github.com/HYOJINPARK/Reuse_VOS.