  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 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. Specifically, after coarse
segmentation masks of the batch of queries are predicted, query-mask pairs act
as pseudo support data to enhance mask predictions mutually, under the guidance
of a simple Group-On Voting module. Comprehensive experiments on three standard
benchmarks show that, in the ONE-shot setting, our Group-On approach
significantly outperforms previous works by considerable margins. For example,
on the COCO-20i dataset, we increase mIoU scores by 8.21% and 7.46% on ASNet
and HSNet baselines, respectively. With only one support image, Group-On can be
even competitive with the counterparts using 5 annotated support images.
