U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation
U-CFR is a novel inference-time framework for interactive segmentation that autonomously self-corrects after each user interaction. It introduces a boundary-aware uncertainty score fusing segmentation uncertainty, contour gradients, and explicit edge predictions to guide internal pseudo-clicks. A dual-head network with segmentation and edge heads enables cascade refinement steps that progressively improve masks. Experiments show over 10% click reduction on challenging datasets like Berkeley.
-->
[Submitted on 22 Jul 2026]
Title:U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation
View a PDF of the paper titled U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation, by Elijah Danquah Darko and 4 other authors
View PDF HTML (experimental)
Abstract:Interactive image segmentation is critical for efficient image annotation; however, existing methods often require many corrective clicks or rely on passive refinement schemes that converge slowly. We propose Uncertainty-Guided Cascade Forward Refinement (U-CFR), a novel inference-time framework that enables models to autonomously self-correct after each user interaction. U-CFR introduces a boundary-aware uncertainty score that fuses segmentation uncertainty, contour gradients, and explicit edge predictions to guide the placement of internal pseudo-clicks. These self-generated clicks target the most ambiguous boundary regions, providing strong corrective signals without additional manual input. To support this process, we design a dual-head network with a shared encoder-decoder backbone: a segmentation head ensures region consistency, while an edge head sharpens boundary alignment. In inference, U-CFR launches a cascade of refinement steps, where each stage leverages the uncertainty-driven pseudo-clicks to refine the mask progressively. Experiments on standard benchmark datasets demonstrate that the proposed U-CFR improves click efficiency, initial mask quality, and boundary accuracy. It reduces the required clicks by over 10% on challenging datasets like Berkeley and offers a more intelligent and efficient interactive annotation.
Comments: 12 pages, 3 figures, 4 tables, ICPR 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.20705 [cs.CV]
(or arXiv:2607.20705v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.20705
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Min Xian [view email] [v1] Wed, 22 Jul 2026 20:23:00 UTC (3,942 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled U-CFR: Uncertainty-Guided Cascade Forward Refinement for Interactive Segmentation, by Elijah Danquah Darko and 4 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-07
Change to browse by:
cs cs.AI
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)