# Interactive Video Object Segmentation in the Wild

arXiv: https://arxiv.org/abs/1801.00269 · HTML: https://mlsanity.com/paper/1801.00269/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2017.

## Built on by (most cited first, 16 of 16)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [A Survey on Deep Learning Technique for Video Segmentation](https://mlsanity.com/paper/2107.01153/) (2021)
- [Modular Interactive Video Object Segmentation: Interaction-to-Mask, Propagation and Difference-Aware Fusion](https://mlsanity.com/paper/2103.07941/) (2021)
- [Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps](https://mlsanity.com/paper/2104.10386/) (2021)
- [DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformer](https://mlsanity.com/paper/2304.06668/) (2023)
- [UCP-Net: Unstructured Contour Points for Instance Segmentation](https://mlsanity.com/paper/2109.07592/) (2021)
- [LiVOS: Light Video Object Segmentation with Gated Linear Matching](https://mlsanity.com/paper/2411.02818/) (2024)
- [Revisiting Click-based Interactive Video Object Segmentation](https://mlsanity.com/paper/2203.01784/) (2022)
- [What is Point Supervision Worth in Video Instance Segmentation?](https://mlsanity.com/paper/2404.01990/) (2024)
- [Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation](https://mlsanity.com/paper/2408.00169/) (2024)
- [Clicking Matters:Towards Interactive Human Parsing](https://mlsanity.com/paper/2111.06162/) (2021)
- [Training-Free Robust Interactive Video Object Segmentation](https://mlsanity.com/paper/2406.05485/) (2024)
- [IDPro: Flexible Interactive Video Object Segmentation by ID-queried Concurrent Propagation](https://mlsanity.com/paper/2401.12480/) (2024)
- [Multi-Granularity Video Object Segmentation](https://mlsanity.com/paper/2412.01471/) (2024)
- [HAISTA-NET: Human Assisted Instance Segmentation Through Attention](https://mlsanity.com/paper/2305.03105/) (2023)
- [FiVOS: A Fish Segmentation Algorithm Based on Interactive Video Object Segmentation and Filter Enhancement](https://mlsanity.com/paper/2610.01480/) (2026)

## Abstract (arXiv)

In this paper we present our system for human-in-the-loop video object segmentation. The backbone of our system is a method for one-shot video object segmentation. While fast, this method requires an accurate pixel-level segmentation of one (or several) frames as input. As manually annotating such a segmentation is impractical, we propose a deep interactive image segmentation method, that can accurately segment objects with only a handful of clicks. On the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on average, setting the new state of the art. Furthermore, as our method iteratively refines an initial segmentation, it can effectively correct frames where the video object segmentation fails, thus allowing users to quickly obtain high quality results even on challenging sequences. Finally, we investigate usage patterns and give insights in how many steps users take to annotate frames, what kind of corrections they provide, etc., thus giving important insights for further improving interactive video segmentation.