Prototype Mixture Models for Few-shot Semantic Segmentation

Understand

Few-shot segmentation is challenging because objects within the support and query images could significantly differ in appearance and pose.

• Using a single prototype acquired directly from the support image to segment the query image causes semantic ambiguity.
• In this paper, we propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation.
• Estimated by an Expectation-Maximization algorithm, PMMs incorporate rich channel-wised and spatial semantics from limited support images.