Hallucination Improves Few-Shot Object Detection

• URL: http://arxiv.org/abs/2105.01294v1
• Date: Tue, 4 May 2021 05:19:53 GMT
• Title: Hallucination Improves Few-Shot Object Detection
• Authors: Weilin Zhang, Yu-Xiong Wang
• Abstract summary: A critical factor in improving few-shot detection is to address the lack of variation in training data. We propose to build a better model of variation for novel classes by transferring the shared within-class variation from base classes. Our approach yields significant performance improvements on two state-of-the-art few-shot detectors with different proposal generation procedures.
• Score: 15.932703834032456
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Learning to detect novel objects from few annotated examples is of great practical importance. A particularly challenging yet common regime occurs when there are extremely limited examples (less than three). One critical factor in improving few-shot detection is to address the lack of variation in training data. We propose to build a better model of variation for novel classes by transferring the shared within-class variation from base classes. To this end, we introduce a hallucinator network that learns to generate additional, useful training examples in the region of interest (RoI) feature space, and incorporate it into a modern object detection model. Our approach yields significant performance improvements on two state-of-the-art few-shot detectors with different proposal generation procedures. In particular, we achieve new state of the art in the extremely-few-shot regime on the challenging COCO benchmark.

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