# UCP-Net: Unstructured Contour Points for Instance Segmentation

arXiv: https://arxiv.org/abs/2109.07592 · HTML: https://mlsanity.com/paper/2109.07592/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [Deep Extreme Cut: From Extreme Points to Object Segmentation](https://mlsanity.com/paper/1711.09081/) (2017)
- [f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation](https://mlsanity.com/paper/2001.10331/) (2020)
- [Large-scale interactive object segmentation with human annotators](https://mlsanity.com/paper/1903.10830/) (2019)
- [Extreme clicking for efficient object annotation](https://mlsanity.com/paper/1708.02750/) (2017)
- [Iteratively Trained Interactive Segmentation](https://mlsanity.com/paper/1805.04398/) (2018)
- [Getting to 99% Accuracy in Interactive Segmentation](https://mlsanity.com/paper/2003.07932/) (2020)
- [Interactive Video Object Segmentation in the Wild](https://mlsanity.com/paper/1801.00269/) (2017)
- [A Fully Convolutional Two-Stream Fusion Network for Interactive Image Segmentation](https://mlsanity.com/paper/1807.02480/) (2018)
- [Iterative Interaction Training for Segmentation Editing Networks](https://mlsanity.com/paper/1807.08555/) (2018)
- [FAIRS -- Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points](https://mlsanity.com/paper/2004.02038/) (2020)

## Built on by (most cited first, 7 of 7)

- [CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation](https://mlsanity.com/paper/2303.05620/) (2023)
- [Adapting the Segment Anything Model During Usage in Novel Situations](https://mlsanity.com/paper/2404.08421/) (2024)
- [Guiding the Guidance: A Comparative Analysis of User Guidance Signals for Interactive Segmentation of Volumetric Images](https://mlsanity.com/paper/2303.06942/) (2023)
- [Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance](https://mlsanity.com/paper/2304.05716/) (2023)
- [PiClick: Picking the desired mask from multiple candidates in click-based interactive segmentation](https://mlsanity.com/paper/2304.11609/) (2023)
- [Strike the Balance: On-the-Fly Uncertainty based User Interactions for Long-Term Video Object Segmentation](https://mlsanity.com/paper/2408.00169/) (2024)
- [Interactive Occlusion Boundary Estimation through Exploitation of Synthetic Data](https://mlsanity.com/paper/2408.15038/) (2024)

## Abstract (arXiv)

The goal of interactive segmentation is to assist users in producing segmentation masks as fast and as accurately as possible. Interactions have to be simple and intuitive and the number of interactions required to produce a satisfactory segmentation mask should be as low as possible. In this paper, we propose a novel approach to interactive segmentation based on unconstrained contour clicks for initial segmentation and segmentation refinement. Our method is class-agnostic and produces accurate segmentation masks (IoU > 85%) for a lower number of user interactions than state-of-the-art methods on popular segmentation datasets (COCO MVal, SBD and Berkeley).