Group-On: Boosting One-Shot Segmentation with Supportive Query

Understand

One-shot semantic segmentation aims to segment query images given only ONE annotated support image of the same class.

• This task is challenging because target objects in the support and query images can be largely different in appearance and pose (i.e., intra-class variation).
• Prior works suggested that incorporating more annotated support images in few-shot settings boosts performances but increases costs due to additional manual labeling.
• In this paper, we propose a novel and effective approach for ONE-shot semantic segmentation, called Group-On, which packs multiple query images in batches for the benefit of mutual knowledge support within the same category.