AcroFOD: An Adaptive Method for Cross-domain Few-shot Object Detection

Yipeng Gao, Lingxiao Yang, Yunmu Huang, Song Xie, Shiyong Li, Weihao Zheng
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引用次数: 10

Abstract

Under the domain shift, cross-domain few-shot object detection aims to adapt object detectors in the target domain with a few annotated target data. There exists two significant challenges: (1) Highly insufficient target domain data; (2) Potential over-adaptation and misleading caused by inappropriately amplified target samples without any restriction. To address these challenges, we propose an adaptive method consisting of two parts. First, we propose an adaptive optimization strategy to select augmented data similar to target samples rather than blindly increasing the amount. Specifically, we filter the augmented candidates which significantly deviate from the target feature distribution in the very beginning. Second, to further relieve the data limitation, we propose the multi-level domain-aware data augmentation to increase the diversity and rationality of augmented data, which exploits the cross-image foreground-background mixture. Experiments show that the proposed method achieves state-of-the-art performance on multiple benchmarks.
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AcroFOD:一种跨域小镜头目标检测的自适应方法
在域移位的情况下,跨域少镜头目标检测的目的是使目标域中的目标检测器具有少量的标注目标数据。存在两大挑战:(1)目标域数据高度不足;(2)目标样本扩增不当,无任何限制,可能造成过度适应和误导。为了应对这些挑战,我们提出了一种由两部分组成的自适应方法。首先,我们提出了一种自适应优化策略,选择与目标样本相似的增强数据,而不是盲目地增加数量。具体来说,我们在一开始就过滤出明显偏离目标特征分布的增强候选对象。其次,为了进一步缓解数据的局限性,我们提出了多层次的领域感知数据增强,利用交叉图像的前景和背景混合来增加增强数据的多样性和合理性。实验表明,该方法在多个基准测试中达到了最先进的性能。
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