Reproduce “FocalClick: Towards Practical Interactive Image Segmentation” on OpenResearch
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About this paper
Xi Chen, Zhiyan Zhao, Yilei Zhang, Manni Duan, Donglian Qi, Hengshuang Zhao
FocalClick introduces an interactive image segmentation method that localizes predictions and updates to improve efficiency and enable effective mask correction for practical applications. This approach drastically reduces computational costs while maintaining competitive accuracy on 'from scratch' annotation and demonstrates superior performance on a new interactive mask correction benchmark.
Key insights
- Decomposing interactive segmentation into coarse global context followed by fine local refinement, coupled with targeted updates, allows for significant efficiency gains without compromising accuracy.
- A specialized merging strategy that preserves existing mask details by only updating user-intended regions is crucial for practical interactive mask correction in real-world annotation workflows.
- Evaluating interactive segmentation models on mask correction tasks, not just 'from scratch' annotation, provides a more comprehensive and industrially relevant measure of their utility.
Summary by alphaXiv.
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