FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter

• URL: http://arxiv.org/abs/2104.00073v1
• Date: Wed, 31 Mar 2021 19:09:43 GMT
• Title: FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter
• Authors: Khoi Nguyen, Sinisa Todorovic
• Abstract summary: We evaluate a new few-shot anchor-free part-based instance segmenter FAPIS. Key novelty is in explicit modeling of latent object parts shared across training object classes. Our evaluation on the benchmark COCO-20i dataset demonstrates that we significantly outperform the state of the art.
• Score: 35.103437828235826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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