# Instance Embedding Transfer to Unsupervised Video Object Segmentation

arXiv: https://arxiv.org/abs/1801.00908 · HTML: https://mlsanity.com/paper/1801.00908/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2018.

## Built on by (most cited first, 19 of 19)

- [Occluded Video Instance Segmentation: A Benchmark](https://mlsanity.com/paper/2102.01558/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Self-supervised Object-Centric Learning for Videos](https://mlsanity.com/paper/2310.06907/) (2023)
- [Guess What Moves: Unsupervised Video and Image Segmentation by Anticipating Motion](https://mlsanity.com/paper/2205.07844/) (2022)
- [Unsupervised Video Object Segmentation via Prototype Memory Network](https://mlsanity.com/paper/2209.03712/) (2022)
- [Moving Object Segmentation: All You Need Is SAM (and Flow)](https://mlsanity.com/paper/2404.12389/) (2024)
- [Unsupervised Multi-object Segmentation by Predicting Probable Motion Patterns](https://mlsanity.com/paper/2210.12148/) (2022)
- [Learning segmentation from point trajectories](https://mlsanity.com/paper/2501.12392/) (2025)
- [Appearance-Based Refinement for Object-Centric Motion Segmentation](https://mlsanity.com/paper/2312.11463/) (2023)
- [Co-attention Propagation Network for Zero-Shot Video Object Segmentation](https://mlsanity.com/paper/2304.03910/) (2023)
- [LOCATE: Self-supervised Object Discovery via Flow-guided Graph-cut and Bootstrapped Self-training](https://mlsanity.com/paper/2308.11239/) (2023)
- [MSN: Efficient Online Mask Selection Network for Video Instance Segmentation](https://mlsanity.com/paper/2106.10452/) (2021)
- [Instance-Level Moving Object Segmentation from a Single Image with Events](https://mlsanity.com/paper/2502.12975/) (2025)
- [Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2501.07806/) (2025)
- [Fast Pixel-Matching for Video Object Segmentation](https://mlsanity.com/paper/2107.04279/) (2021)
- [ClickVOS: Click Video Object Segmentation](https://mlsanity.com/paper/2403.06130/) (2024)
- [Physics Driven Domain Specific Transporter Framework with Attention Mechanism for Ultrasound Imaging](https://mlsanity.com/paper/2109.06346/) (2021)
- [GISE-TTT:A Framework for Global InformationSegmentation and Enhancement](https://mlsanity.com/paper/2504.00879/) (2025)
- [EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras](https://mlsanity.com/paper/2506.06596/) (2025)

## Abstract (arXiv)

We propose a method for unsupervised video object segmentation by transferring the knowledge encapsulated in image-based instance embedding networks. The instance embedding network produces an embedding vector for each pixel that enables identifying all pixels belonging to the same object. Though trained on static images, the instance embeddings are stable over consecutive video frames, which allows us to link objects together over time. Thus, we adapt the instance networks trained on static images to video object segmentation and incorporate the embeddings with objectness and optical flow features, without model retraining or online fine-tuning. The proposed method outperforms state-of-the-art unsupervised segmentation methods in the DAVIS dataset and the FBMS dataset.