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FocalClick-XL: Towards Unified and High-quality Interactive Segmentation.

Xi Chen, Hengshuang Zhao

IEEE Transactions on Pattern Analysis and Machine Intelligence

|May 22, 2026

Summary

This summary is machine-generated.

FocalClick-XL enhances interactive segmentation by decomposing tasks into context, object, and detail levels. This novel approach achieves state-of-the-art results across various interaction types and generates fine-detailed alpha mattes.

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Area of Science:

Computer Vision
Machine Learning

Background:

Interactive segmentation allows users to define objects using simple inputs like clicks or scribbles.
Existing methods often lack flexibility in interaction types and precision in detail extraction.

Purpose of the Study:

To introduce FocalClick-XL, a novel interactive segmentation pipeline addressing limitations of prior methods.
To enhance flexibility and performance across diverse interaction modalities.

Main Methods:

Decomposition of interactive segmentation into meta-tasks: context, object, and detail.
Dedicated subnets for each level with scaled, independent pretraining.
Shared information across interaction forms and a prompting layer for object-level encoding.

Main Results:

Achieved state-of-the-art performance on click-based interactive segmentation benchmarks.
Demonstrated adaptability to various interaction formats: clicks, boxes, scribbles, and coarse masks.
Successfully generated high-fidelity alpha mattes with fine-grained details.

Conclusions:

FocalClick-XL offers a versatile and powerful solution for interactive segmentation and alpha matting.
The proposed meta-task decomposition and pretraining strategy significantly improve performance and flexibility.