# MOVE: Unsupervised Movable Object Segmentation and Detection

arXiv: https://arxiv.org/abs/2210.07920 · HTML: https://mlsanity.com/paper/2210.07920/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 58 of its 96 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 30 of 58)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [MONet: Unsupervised Scene Decomposition and Representation](https://mlsanity.com/paper/1901.11390/) (2019)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [Localizing Objects with Self-Supervised Transformers and no Labels](https://mlsanity.com/paper/2109.14279/) (2021)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Attend, Infer, Repeat: Fast Scene Understanding with Generative Models](https://mlsanity.com/paper/1603.08575/) (2016)
- [Projected GANs Converge Faster](https://mlsanity.com/paper/2111.01007/) (2021)
- [Visual Saliency Based on Multiscale Deep Features](https://mlsanity.com/paper/1503.08663/) (2015)
- [Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut](https://mlsanity.com/paper/2202.11539/) (2022)
- [Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localization](https://mlsanity.com/paper/2205.07839/) (2022)
- [FreeSOLO: Learning to Segment Objects without Annotations](https://mlsanity.com/paper/2202.12181/) (2022)
- [Self-Supervised Learning of Object Parts for Semantic Segmentation](https://mlsanity.com/paper/2204.13101/) (2022)
- [The Fast Bilateral Solver](https://mlsanity.com/paper/1511.03296/) (2015)

## Built on by (most cited first, 20 of 20)

- [Diffusion Models for Open-Vocabulary Segmentation](https://mlsanity.com/paper/2306.09316/) (2023)
- [CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free](https://mlsanity.com/paper/2309.14289/) (2023)
- [u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model](https://mlsanity.com/paper/2311.05348/) (2023)
- [Does Object Binding Naturally Emerge in Large Pretrained Vision Transformers?](https://mlsanity.com/paper/2510.24709/) (2025)
- [Unsupervised Object Localization in the Era of Self-Supervised ViTs: A Survey](https://mlsanity.com/paper/2310.12904/) (2023)
- [Betrayed by Attention: A Simple yet Effective Approach for Self-supervised Video Object Segmentation](https://mlsanity.com/paper/2311.17893/) (2023)
- [Understanding Self-Supervised Features for Learning Unsupervised Instance Segmentation](https://mlsanity.com/paper/2311.14665/) (2023)
- [Instance-Level Moving Object Segmentation from a Single Image with Events](https://mlsanity.com/paper/2502.12975/) (2025)
- [SEMPART: Self-supervised Multi-resolution Partitioning of Image Semantics](https://mlsanity.com/paper/2309.10972/) (2023)
- [Aerial Image Object Detection With Vision Transformer Detector (ViTDet)](https://mlsanity.com/paper/2301.12058/) (2023)
- [Box-based Refinement for Weakly Supervised and Unsupervised Localization Tasks](https://mlsanity.com/paper/2309.03874/) (2023)
- [Towards End-to-End Unsupervised Saliency Detection with Self-Supervised Top-Down Context](https://mlsanity.com/paper/2310.09533/) (2023)
- [FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching](https://mlsanity.com/paper/2505.13174/) (2025)
- [Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots](https://mlsanity.com/paper/2304.01430/) (2023)
- [A Lightweight Clustering Framework for Unsupervised Semantic Segmentation](https://mlsanity.com/paper/2311.18628/) (2023)
- [Monocular Per-Object Distance Estimation with Masked Object Modeling](https://mlsanity.com/paper/2401.03191/) (2024)
- [Rethinking Saliency-Guided Weakly-Supervised Semantic Segmentation](https://mlsanity.com/paper/2404.00918/) (2024)
- [Joint-Embedding Predictive Architecture for Self-Supervised Learning of Mask Classification Architecture](https://mlsanity.com/paper/2407.10733/) (2024)
- [Object-Centric Cropping for Visual Few-Shot Classification](https://mlsanity.com/paper/2508.00218/) (2025)
- [Self-supervised Automatic Matting](https://mlsanity.com/paper/2607.10395/) (2026)

## Abstract (arXiv)

We introduce MOVE, a novel method to segment objects without any form of supervision. MOVE exploits the fact that foreground objects can be shifted locally relative to their initial position and result in realistic (undistorted) new images. This property allows us to train a segmentation model on a dataset of images without annotation and to achieve state of the art (SotA) performance on several evaluation datasets for unsupervised salient object detection and segmentation. In unsupervised single object discovery, MOVE gives an average CorLoc improvement of 7.2% over the SotA, and in unsupervised class-agnostic object detection it gives a relative AP improvement of 53% on average. Our approach is built on top of self-supervised features (e.g. from DINO or MAE), an inpainting network (based on the Masked AutoEncoder) and adversarial training.