Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
- URL: http://arxiv.org/abs/2007.09384v1
- Date: Sat, 18 Jul 2020 09:48:29 GMT
- Title: Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
- Authors: Jiaxi Wu, Songtao Liu, Di Huang, Yunhong Wang
- Abstract summary: Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances.
We propose a Multi-scale Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD.
MPSR generates multi-scale positive samples as object pyramids and refines the prediction at various scales.
- Score: 61.60255654558682
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Few-shot object detection (FSOD) helps detectors adapt to unseen classes with
few training instances, and is useful when manual annotation is time-consuming
or data acquisition is limited. Unlike previous attempts that exploit few-shot
classification techniques to facilitate FSOD, this work highlights the
necessity of handling the problem of scale variations, which is challenging due
to the unique sample distribution. To this end, we propose a Multi-scale
Positive Sample Refinement (MPSR) approach to enrich object scales in FSOD. It
generates multi-scale positive samples as object pyramids and refines the
prediction at various scales. We demonstrate its advantage by integrating it as
an auxiliary branch to the popular architecture of Faster R-CNN with FPN,
delivering a strong FSOD solution. Several experiments are conducted on PASCAL
VOC and MS COCO, and the proposed approach achieves state of the art results
and significantly outperforms other counterparts, which shows its
effectiveness. Code is available at https://github.com/jiaxi-wu/MPSR.
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