# Unsupervised Moving Object Detection via Contextual Information Separation

arXiv: https://arxiv.org/abs/1901.03360 · HTML: https://mlsanity.com/paper/1901.03360/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2019.

## Built on by (most cited first, 30 of 72)

- [Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals](https://mlsanity.com/paper/2102.06191/) (2021)
- [Self-supervised Video Object Segmentation by Motion Grouping](https://mlsanity.com/paper/2104.07658/) (2021)
- [Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos](https://mlsanity.com/paper/2205.14065/) (2022)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [TokenCut: Segmenting Objects in Images and Videos with Self-supervised Transformer and Normalized Cut](https://mlsanity.com/paper/2209.00383/) (2022)
- [ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild](https://mlsanity.com/paper/2207.09137/) (2022)
- [NeRF-SOS: Any-View Self-supervised Object Segmentation on Complex Scenes](https://mlsanity.com/paper/2209.08776/) (2022)
- [Segmenting Moving Objects via an Object-Centric Layered Representation](https://mlsanity.com/paper/2207.02206/) (2022)
- [Deformable Sprites for Unsupervised Video Decomposition](https://mlsanity.com/paper/2204.07151/) (2022)
- [Breaking the "Object" in Video Object Segmentation](https://mlsanity.com/paper/2212.06200/) (2022)
- [The Emergence of Objectness: Learning Zero-Shot Segmentation from Videos](https://mlsanity.com/paper/2111.06394/) (2021)
- [Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion](https://mlsanity.com/paper/2205.07844/) (2022)
- [VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation](https://mlsanity.com/paper/2308.14710/) (2023)
- [Segment Any Motion in Videos](https://mlsanity.com/paper/2503.22268/) (2025)
- [EM-driven unsupervised learning for efficient motion segmentation](https://mlsanity.com/paper/2201.02074/) (2022)
- [Unsupervised Object Learning via Common Fate](https://mlsanity.com/paper/2110.06562/) (2021)
- [Unsupervised Foreground Extraction via Deep Region Competition](https://mlsanity.com/paper/2110.15497/) (2021)
- [OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds](https://mlsanity.com/paper/2210.04458/) (2022)
- [Learning Representational Invariances for Data-Efficient Action Recognition](https://mlsanity.com/paper/2103.16565/) (2021)
- [Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation](https://mlsanity.com/paper/2210.00489/) (2022)
- [Learning Semantic-Aware Dynamics for Video Prediction](https://mlsanity.com/paper/2104.09762/) (2021)
- [FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation](https://mlsanity.com/paper/2202.08141/) (2022)
- [MOVE: Unsupervised Movable Object Segmentation and Detection](https://mlsanity.com/paper/2210.07920/) (2022)
- [Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos](https://mlsanity.com/paper/2308.09951/) (2023)
- [GANSeg: Learning to Segment by Unsupervised Hierarchical Image Generation](https://mlsanity.com/paper/2112.01036/) (2021)
- [Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns](https://mlsanity.com/paper/2210.12148/) (2022)
- [AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints](https://mlsanity.com/paper/2205.10636/) (2022)
- [Multi-Object Discovery by Low-Dimensional Object Motion](https://mlsanity.com/paper/2307.08027/) (2023)
- [Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey](https://mlsanity.com/paper/2310.12904/) (2023)
- [A Simple and Powerful Global Optimization for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2209.09341/) (2022)

## Abstract (arXiv)

We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible. The result is a model where hypotheses naturally compete with no need for explicit regularization or hyper-parameter tuning. Although our method requires no supervision whatsoever, it outperforms several methods that are pre-trained on large annotated datasets. Our model can be thought of as a generalization of classical variational generative region-based segmentation, but in a way that avoids explicit regularization or solution of partial differential equations at run-time.