  This paper is about few-shot instance segmentation, where training and test
image sets do not share the same object classes. We specify and evaluate a new
few-shot anchor-free part-based instance segmenter FAPIS. Our key novelty is in
explicit modeling of latent object parts shared across training object classes,
which is expected to facilitate our few-shot learning on new classes in
testing. We specify a new anchor-free object detector aimed at scoring and
regressing locations of foreground bounding boxes, as well as estimating
relative importance of latent parts within each box. Also, we specify a new
network for delineating and weighting latent parts for the final instance
segmentation within every detected bounding box. Our evaluation on the
benchmark COCO-20i dataset demonstrates that we significantly outperform the
state of the art.
