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