# Video Propagation Networks

arXiv: https://arxiv.org/abs/1612.05478 · HTML: https://mlsanity.com/paper/1612.05478/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Dec 2016.

## Built on by (most cited first, 30 of 45)

- [MOSE: A New Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2302.01872/) (2023)
- [Layered Neural Atlases for Consistent Video Editing](https://mlsanity.com/paper/2109.11418/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [CoDeF: Content Deformation Fields for Temporally Consistent Video Processing](https://mlsanity.com/paper/2308.07926/) (2023)
- [SSTVOS: Sparse Spatiotemporal Transformers for Video Object Segmentation](https://mlsanity.com/paper/2101.08833/) (2021)
- [MOSEv2: A More Challenging Dataset for Video Object Segmentation in Complex Scenes](https://mlsanity.com/paper/2508.05630/) (2025)
- [Local-Global Context Aware Transformer for Language-Guided Video Segmentation](https://mlsanity.com/paper/2203.09773/) (2022)
- [Active Boundary Loss for Semantic Segmentation](https://mlsanity.com/paper/2102.02696/) (2021)
- [Generative Video Propagation](https://mlsanity.com/paper/2412.19761/) (2024)
- [Locality-Aware Inter-and Intra-Video Reconstruction for Self-Supervised Correspondence Learning](https://mlsanity.com/paper/2203.14333/) (2022)
- [PVO: Panoptic Visual Odometry](https://mlsanity.com/paper/2207.01610/) (2022)
- [Temporally Consistent Video Colorization with Deep Feature Propagation and Self-regularization Learning](https://mlsanity.com/paper/2110.04562/) (2021)
- [Event-assisted Low-Light Video Object Segmentation](https://mlsanity.com/paper/2404.01945/) (2024)
- [Learning the What and How of Annotation in Video Object Segmentation](https://mlsanity.com/paper/2311.04414/) (2023)
- [Deep Video Prior for Video Consistency and Propagation](https://mlsanity.com/paper/2201.11632/) (2022)
- [Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentation](https://mlsanity.com/paper/2303.10100/) (2023)
- [SiamMask: A Framework for Fast Online Object Tracking and Segmentation](https://mlsanity.com/paper/2207.02088/) (2022)
- [VCGAN: Video Colorization with Hybrid Generative Adversarial Network](https://mlsanity.com/paper/2104.12357/) (2021)
- [Text-Driven Stylization of Video Objects](https://mlsanity.com/paper/2206.12396/) (2022)
- [Video Colorization with Pre-trained Text-to-Image Diffusion Models](https://mlsanity.com/paper/2306.01732/) (2023)
- [INVE: Interactive Neural Video Editing](https://mlsanity.com/paper/2307.07663/) (2023)
- [LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation](https://mlsanity.com/paper/2409.05847/) (2024)
- [NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing](https://mlsanity.com/paper/2406.06523/) (2024)
- [Temporal Transductive Inference for Few-Shot Video Object Segmentation](https://mlsanity.com/paper/2203.14308/) (2022)
- [Normal-guided Garment UV Prediction for Human Re-texturing](https://mlsanity.com/paper/2303.06504/) (2023)
- [Hashing Neural Video Decomposition with Multiplicative Residuals in Space-Time](https://mlsanity.com/paper/2309.14022/) (2023)
- [Video TokenCom: Textual Intent-Guided Multi-Rate Video Token Communications with UEP-Based Adaptive Source-Channel Coding](https://mlsanity.com/paper/2603.02470/) (2026)
- [Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency](https://mlsanity.com/paper/2303.15081/) (2023)
- [TAM-VT: Transformation-Aware Multi-scale Video Transformer for Segmentation and Tracking](https://mlsanity.com/paper/2312.08514/) (2023)
- [Studying Image Diffusion Features for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2504.05468/) (2025)

## Abstract (arXiv)

We propose a technique that propagates information forward through video data. The method is conceptually simple and can be applied to tasks that require the propagation of structured information, such as semantic labels, based on video content. We propose a 'Video Propagation Network' that processes video frames in an adaptive manner. The model is applied online: it propagates information forward without the need to access future frames. In particular we combine two components, a temporal bilateral network for dense and video adaptive filtering, followed by a spatial network to refine features and increased flexibility. We present experiments on video object segmentation and semantic video segmentation and show increased performance comparing to the best previous task-specific methods, while having favorable runtime. Additionally we demonstrate our approach on an example regression task of color propagation in a grayscale video.