Multi-scale Positive Sample Refinement for Few-Shot Object Detection
Autor: | Yunhong Wang, Di Huang, Songtao Liu, Jiaxi Wu |
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Rok vydání: | 2020 |
Předmět: |
Exploit
Computer science Scale (descriptive set theory) 02 engineering and technology Pascal (programming language) 010501 environmental sciences Object (computer science) computer.software_genre 01 natural sciences Object detection Data acquisition Sampling distribution 0202 electrical engineering electronic engineering information engineering Code (cryptography) 020201 artificial intelligence & image processing Data mining computer 0105 earth and related environmental sciences computer.programming_language |
Zdroj: | Computer Vision – ECCV 2020 ISBN: 9783030585167 ECCV (16) |
Popis: | 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. |
Databáze: | OpenAIRE |
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