# CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation

arXiv: https://arxiv.org/abs/2303.05620 · HTML: https://mlsanity.com/paper/2303.05620/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Reviving Iterative Training with Mask Guidance for Interactive Segmentation](https://mlsanity.com/paper/2102.06583/) (2021)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [f-BRS: Rethinking Backpropagating Refinement for Interactive Segmentation](https://mlsanity.com/paper/2001.10331/) (2020)
- [SimpleClick: Interactive Image Segmentation with Simple Vision Transformers](https://mlsanity.com/paper/2210.11006/) (2022)
- [FocalClick: Towards Practical Interactive Image Segmentation](https://mlsanity.com/paper/2204.02574/) (2022)
- [AdaptIS: Adaptive Instance Selection Network](https://mlsanity.com/paper/1909.07829/) (2019)
- [Iteratively Trained Interactive Segmentation](https://mlsanity.com/paper/1805.04398/) (2018)
- [PseudoClick: Interactive Image Segmentation with Click Imitation](https://mlsanity.com/paper/2207.05282/) (2022)
- [UCP-Net: Unstructured Contour Points for Instance Segmentation](https://mlsanity.com/paper/2109.07592/) (2021)
- [Neutro-Connectedness Cut](https://mlsanity.com/paper/1512.06285/) (2015)

## Built on by (most cited first, 17 of 17)

- [VDD: Varied Drone Dataset for Semantic Segmentation](https://mlsanity.com/paper/2305.13608/) (2023)
- [COCONut: Modernizing COCO Segmentation](https://mlsanity.com/paper/2404.08639/) (2024)
- [PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts](https://mlsanity.com/paper/2404.15028/) (2024)
- [Repurposing Stable Diffusion Attention for Training-Free Unsupervised Interactive Segmentation](https://mlsanity.com/paper/2411.10411/) (2024)
- [RClicks: Realistic Click Simulation for Benchmarking Interactive Segmentation](https://mlsanity.com/paper/2410.11722/) (2024)
- [DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation](https://mlsanity.com/paper/2506.23104/) (2025)
- [PVPUFormer: Probabilistic Visual Prompt Unified Transformer for Interactive Image Segmentation](https://mlsanity.com/paper/2306.06656/) (2023)
- [MST: Adaptive Multi-Scale Tokens Guided Interactive Segmentation](https://mlsanity.com/paper/2401.04403/) (2024)
- [Structured Click Control in Transformer-based Interactive Segmentation](https://mlsanity.com/paper/2405.04009/) (2024)
- [Interactive 3D Segmentation for Primary Gross Tumor Volume in Oropharyngeal Cancer](https://mlsanity.com/paper/2409.06605/) (2024)
- [M2N2V2: Multi-Modal Unsupervised and Training-free Interactive Segmentation](https://mlsanity.com/paper/2503.16254/) (2025)
- [Deep learning-based interactive segmentation in remote sensing](https://mlsanity.com/paper/2308.13174/) (2023)
- [RefCut: Interactive Segmentation with Reference Guidance](https://mlsanity.com/paper/2503.17820/) (2025)
- [CasPoinTr: Point Cloud Completion with Cascaded Networks and Knowledge Distillation](https://mlsanity.com/paper/2509.23375/) (2025)
- [Uncertainty-Aware Extreme Point Tracing for Weakly Supervised Ultrasound Image Segmentation](https://mlsanity.com/paper/2510.15666/) (2025)
- [VFM-ISRefiner: Towards Better Adapting Vision Foundation Models for Interactive Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2512.00718/) (2025)
- [ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation](https://mlsanity.com/paper/2603.11498/) (2026)

## Abstract (arXiv)

The click-based interactive segmentation aims to extract the object of interest from an image with the guidance of user clicks. Recent work has achieved great overall performance by employing feedback from the output. However, in most state-of-the-art approaches, 1) the inference stage involves inflexible heuristic rules and requires a separate refinement model, and 2) the number of user clicks and model performance cannot be balanced. To address the challenges, we propose a click-based and mask-guided interactive image segmentation framework containing three novel components: Cascade-Forward Refinement (CFR), Iterative Click Loss (ICL), and SUEM image augmentation. The CFR offers a unified inference framework to generate segmentation results in a coarse-to-fine manner. The proposed ICL allows model training to improve segmentation and reduce user interactions simultaneously. The proposed SUEM augmentation is a comprehensive way to create large and diverse training sets for interactive image segmentation. Extensive experiments demonstrate the state-of-the-art performance of the proposed approach on five public datasets. Remarkably, our model reduces by 33.2%, and 15.5% the number of clicks required to surpass an IoU of 0.95 in the previous state-of-the-art approach on the Berkeley and DAVIS sets, respectively.