# Scale-aware multi-level guidance for interactive instance segmentation

## TL;DR

This paper introduces a scale-aware guidance map transformation for interactive instance segmentation, enabling basic FCNs to outperform complex models by leveraging hierarchical image structure.

## Contribution

The authors propose a novel click-to-guidance transformation that incorporates scale-awareness and hierarchical information, improving segmentation performance.

## Key findings

- Outperforms existing methods on four benchmarks
- Enables basic FCNs to surpass state-of-the-art results
- Significantly improves segmentation accuracy with simple models

## Abstract

In interactive instance segmentation, users give feedback to iteratively refine segmentation masks. The user-provided clicks are transformed into guidance maps which provide the network with necessary cues on the whereabouts of the object of interest. Guidance maps used in current systems are purely distance-based and are either too localized or non-informative. We propose a novel transformation of user clicks to generate scale-aware guidance maps that leverage the hierarchical structural information present in an image. Using our guidance maps, even the most basic FCNs are able to outperform existing approaches that require state-of-the-art segmentation networks pre-trained on large scale segmentation datasets. We demonstrate the effectiveness of our proposed transformation strategy through comprehensive experimentation in which we significantly raise state-of-the-art on four standard interactive segmentation benchmarks.