# Fast Interactive Video Object Segmentation with Graph Neural Networks

arXiv: https://arxiv.org/abs/2103.03821 · HTML: https://mlsanity.com/paper/2103.03821/

## Key facts

- Cited by 4 papers in the map (data through 7 Oct 2026).
- Builds on 25 of its 51 references that are in the map.
- First posted on arXiv 5 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Video Object Segmentation using Space-Time Memory Networks](https://mlsanity.com/paper/1904.00607/) (2019)
- [YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark](https://mlsanity.com/paper/1809.03327/) (2018)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Videos as Space-Time Region Graphs](https://mlsanity.com/paper/1806.01810/) (2018)
- [Learning a Neural Solver for Multiple Object Tracking](https://mlsanity.com/paper/1912.07515/) (2019)
- [Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++](https://mlsanity.com/paper/1803.09693/) (2018)
- [Zero-Shot Video Object Segmentation via Attentive Graph Neural Networks](https://mlsanity.com/paper/2001.06807/) (2020)
- [Video Object Segmentation and Tracking: A Survey](https://mlsanity.com/paper/1904.09172/) (2019)
- [The 2018 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1803.00557/) (2018)
- [Memory Aggregation Networks for Efficient Interactive Video Object Segmentation](https://mlsanity.com/paper/2003.13246/) (2020)
- [Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks](https://mlsanity.com/paper/1904.09791/) (2019)
- [Interactive Video Object Segmentation Using Global and Local Transfer Modules](https://mlsanity.com/paper/2007.08139/) (2020)
- [Unsupervised Video Object Segmentation for Deep Reinforcement Learning](https://mlsanity.com/paper/1805.07780/) (2018)
- [Learning Video Instance Segmentation with Recurrent Graph Neural Networks](https://mlsanity.com/paper/2012.03911/) (2020)

## Built on by (most cited first, 4 of 4)

- [Multimodal learning with graphs](https://mlsanity.com/paper/2209.03299/) (2022)
- [Exploring Causal Learning through Graph Neural Networks: An In-depth Review](https://mlsanity.com/paper/2311.14994/) (2023)
- [IDPro: Flexible Interactive Video Object Segmentation by ID-queried Concurrent Propagation](https://mlsanity.com/paper/2401.12480/) (2024)
- [Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas](https://mlsanity.com/paper/2502.15907/) (2025)

## Abstract (arXiv)

Pixelwise annotation of image sequences can be very tedious for humans. Interactive video object segmentation aims to utilize automatic methods to speed up the process and reduce the workload of the annotators. Most contemporary approaches rely on deep convolutional networks to collect and process information from human annotations throughout the video. However, such networks contain millions of parameters and need huge amounts of labeled training data to avoid overfitting. Beyond that, label propagation is usually executed as a series of frame-by-frame inference steps, which is difficult to be parallelized and is thus time consuming. In this paper we present a graph neural network based approach for tackling the problem of interactive video object segmentation. Our network operates on superpixel-graphs which allow us to reduce the dimensionality of the problem by several magnitudes. We show, that our network possessing only a few thousand parameters is able to achieve state-of-the-art performance, while inference remains fast and can be trained quickly with very little data.