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摘要: 在小样本目标检测领域, “训练和微调”两阶段表征学习范式因学习策略简单, 应用广泛. 然而, 通过探索性实验发现, 基于该范式的模型容易将新类别实例错误地分类为背景类, 从而降低对新类的识别能力. 为解决这一问题, 提出构造一个正则化分类器, 并使用“最小化背景判别性知识的调节器(BDKMR)”来引导分类器训练. BDKMR通过“最小化背景判别性知识的交叉$l_p $正则项”显式地减少背景判别性知识对构建新类分类器的干扰, 并利用“权重范数管理器”调节分类器中各类别的权重范数, 以提高模型对新类别的关注度, 同时降低其对背景类别的偏好. 此外, 考虑到 BDKMR 可能改变特征空间分布, 提出“分类器解耦模块”, 以调控模型微调过程中正则化分类器对特征提取器学习的影响. 多个数据集上的实验结果表明, 所提出的方法能够有效减少模型对新类实例的错误分类, 进而显著提升对新类的检测性能.Abstract: In the field of few-shot object detection, the “training and fine-tuning” two-stage representation learning framework is widely used due to the simplicity of its learning strategy. However, through exploratory experiments, we demonstrate that this learning paradigm is prone to misclassify novel instances as background instances, which hinders the ability of the model to recognize novel object instances. To address this issue, we propose that construct a regularized classifier and use the background discriminative knowledge minimizing regulator (BDKMR) to guide the classifier training. BDKMR explicitly reduces the effect of background discriminative knowledge on the classifier for novel categories by employing the background discriminative knowledge minimizing cross-$l_p $ regularization. Moreover, BDKMR uses the weight norm manager to adjust the weight norm of each category in the classifier in order to enhance the model's attention to new categories, while alleviating its bias toward the background category. Additionally, considering that BDKMR can alter the feature space distribution, the decoupled box classifier module is introduced to adjust the impact of the regulator on the feature extractor during the fine-tuning stage. Experimental results on multiple datasets validate that the proposed method effectively reduces the misclassification of novel object instances and improves the performance of novel categories.
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Key words:
- Few-shot learning /
- few-shot object detection /
- regularization /
- discriminative knowledge
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图 3 新类的权重范数平均值和正权重范数平均值随训练迭代次数的变化
Fig. 3 The variation of the average norm of weights and the average norm of positive weights for novel classes with respect to the number of training iterations
图 4 不同类别权重范数与正权重范数的比较
Fig. 4 Comparisons of weight norms and positive weight norms between different categories
图 5 前景类原型与背景类原型的相似度分析
Fig. 5 Analysis of the similarity between prototypes of foreground classes and background classes
图 6 前景类别与背景类别的分类器权重相似度分析
Fig. 6 Similarity analysis of classifier weights for foreground and background categories
图 8 不同样本数设置下新类与背景类权重的相似度比较
Fig. 8 Comparison of weight similarity between novel and background categories under different shot settings
图 9 DeFRCN和BDKMR的分类结果对比
Fig. 9 Comparison of classification results between DeFRCN and BDKMR
图 10 BDKMR相较于DeFRCN的新类−背景类权重相似度变化
Fig. 10 Changes of weight similarity between novel and background classes: BDKMR compared to DeFRCN
图 11 BDKMR和DeFRCN检测结果的可视化对比
Fig. 11 Visual comparison of detection results between BDKMR and DeFRCN
表 1 COCO数据集中新类的小样本目标检测性能(%)
Table 1 FSOD performances of novel categories on COCO dataset (%)
方法 样本数 1 2 3 5 10 30 FSRW[22] — — — — 5.6 9.1 TFA[14] 3.4 4.6 6.6 8.3 10.0 13.7 MPSR[65] 2.3 3.5 5.2 6.7 9.8 14.1 FSCE[25] — — — — 11.9 16.4 SRR-FSD[16] — — — — 11.3 14.7 DeFRCN[39] 6.5 11.8 13.4 15.3 18.6 22.5 FCT[35] 5.6 7.9 11.1 14.0 17.1 21.4 BDKMR (本文) 9.8 13.3 13.8 15.5 18.1 21.6 注: 加粗字体表示各列最优结果. 
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表 2 VOC数据集中新类的小样本目标检测性能(%)
Table 2 FSOD performances of the novel categories on VOC dataset (%)
方法 新类分割1 新类分割2 新类分割3 1 2 3 5 10 1 2 3 5 10 1 2 3 5 10 TFA[14] 39.8 36.1 44.7 55.7 56.0 23.5 26.9 34.1 35.1 39.1 30.8 34.8 42.8 49.5 49.8 MPSR[65] 41.7 42.5 51.4 55.2 61.8 24.4 29.3 39.2 39.9 47.8 35.6 41.8 42.3 48.0 49.7 FSCE[25] 44.2 43.8 51.4 61.9 63.4 27.3 29.5 43.5 44.2 50.2 37.2 41.9 47.5 54.6 58.5 SRR-FSD[16] 47.8 50.5 51.3 55.2 56.8 32.5 35.3 39.1 40.8 43.8 40.1 41.5 44.3 46.9 46.4 DeFRCN*[39] 55.1 61.9 64.9 65.8 66.2 33.8 45.1 46.1 53.2 52.3 51.0 56.6 55.6 59.7 61.9 Meta Faster R-CNN[41] 43.0 54.5 60.6 66.1 65.4 27.7 35.5 46.1 47.8 51.4 40.6 46.4 53.4 59.9 58.6 FCT[35] 49.9 57.1 57.9 63.2 67.1 27.6 34.5 43.7 49.2 51.2 39.5 54.7 52.3 57.0 58.7 Pseudo-Labelling[40] 54.5 53.2 58.8 63.2 65.7 32.8 29.2 50.7 49.8 50.6 48.4 52.7 55.0 59.6 59.6 ICPE[37] 54.3 59.5 62.4 65.7 66.2 33.5 40.1 48.7 51.7 52.5 50.9 53.1 55.3 60.6 60.1 $\sigma$-ADP[36] 52.3 55.5 63.1 65.9 66.7 42.7 45.8 48.7 54.8 56.3 47.8 51.8 56.8 60.3 62.4 BDKMR (本文) 58.3 65.1 67.2 67.8 66.6 37.6 47.2 48.9 53.8 52.3 55.2 58.8 57.8 60.9 62.5
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表 4 在VOC数据集的30个随机训练样本上的实验结果(%)
Table 4 Experimental results over 30 random training samples on VOC dataset (%)
方法 新类分割1 新类分割2 新类分割3 1 2 3 5 10 1 2 3 5 10 1 2 3 5 10 FRCN+ft-full[33] 9.9 15.6 21.6 28.0 35.6 9.4 13.8 17.4 21.9 29.8 8.1 13.9 19.0 23.9 31.0 Xiao等[15] 24.2 35.3 42.2 49.1 57.4 21.6 24.6 31.9 37.0 45.7 21.2 30.0 37.2 43.8 49.6 TFA[14] 25.3 36.4 42.1 47.9 52.8 18.3 27.5 30.9 34.1 39.5 17.9 27.2 34.3 40.8 45.6 FSCE[25] 32.9 44.0 46.8 52.9 59.7 23.7 30.6 38.4 43.0 48.5 22.6 33.4 39.5 47.3 54.0 DeFRCN*[39] 39.3 50.9 55.3 61.8 65.3 27.4 36.8 40.4 45.1 50.8 35.0 45.1 50.2 55.7 58.9 DCNet[34] 33.9 37.4 43.7 51.1 59.6 23.2 24.8 30.6 36.7 46.6 32.3 34.9 39.7 42.6 50.7 FCT[35] 38.5 49.6 53.5 59.8 64.3 25.9 34.2 40.1 44.9 47.4 34.7 43.9 49.3 53.1 56.3 $\sigma$-ADP[36] 35.9 40.3 49.8 56.8 65.1 25.6 30.3 41.7 41.8 50.3 33.9 35.6 43.5 47.1 55.9 BDKMR (本文) 43.6 54.3 58.1 63.1 66.4 30.1 38.2 41.5 45.7 51.1 39.9 48.3 52.4 57.5 59.8 $\pm$3.2 $\pm$2.0 $\pm$1.8 $\pm$1.1 $\pm$1.0 $\pm$2.7 $\pm$2.1 $\pm$1.7 $\pm$1.9 $\pm$1.0 $\pm$3.2 $\pm$2.2 $\pm$1.5 $\pm$1.1 $\pm$1.1
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表 3 在COCO数据集10个随机训练样本上的实验结果(%)
Table 3 Experimental results over 10 random training samples on COCO dataset (%)
样本数 方法 新类 nAP nAP50 nAP75 1 FRCN+ft-full[33] 1.7$\pm$0.2 3.3$\pm$0.3 1.6$\pm$0.2 TFA[14] 1.9$\pm$0.4 3.8$\pm$0.6 1.7$\pm$0.5 DeFRCN[39] 4.8$\pm$0.6 9.5$\pm$0.9 4.4$\pm$0.8 BDKMR (本文) 7.3$\pm$0.6 14.7$\pm$0.9 6.5$\pm$0.8 2 FRCN+ft-full[33] 3.1$\pm$0.3 6.1$\pm$0.6 2.9$\pm$0.3 TFA[14] 3.9$\pm$0.4 7.8$\pm$0.7 3.6$\pm$0.6 DeFRCN[39] 8.5$\pm$0.9 16.3$\pm$1.4 7.8$\pm$1.1 BDKMR (本文) 10.7$\pm$0.7 20.6$\pm$1.1 9.9$\pm$0.9 3 FRCN+ft-full[33] 3.7$\pm$0.4 7.1$\pm$0.8 3.5$\pm$0.4 TFA[14] 5.1$\pm$0.6 9.9$\pm$0.9 4.8$\pm$0.6 DeFRCN[39] 10.7$\pm$0.7 20.0$\pm$1.2 10.3$\pm$0.8 BDKMR (本文) 12.4$\pm$0.4 23.2$\pm$0.9 11.8$\pm$0.5 5 FRCN+ft-full[33] 4.6$\pm$0.5 8.7$\pm$1.0 4.4$\pm$0.6 TFA[14] 7.0$\pm$0.7 13.3$\pm$1.2 6.5$\pm$0.7 DeFRCN[39] 13.5$\pm$0.6 24.7$\pm$1.1 13.0$\pm$0.6 BDKMR (本文) 14.3$\pm$0.5 26.5$\pm$1.2 13.8$\pm$0.5 10 FRCN+ft-full[33] 5.5$\pm$0.9 10.0$\pm$1.6 5.5$\pm$0.9 TFA[14] 9.1$\pm$0.5 17.1$\pm$1.1 8.8$\pm$0.5 DeFRCN[39] 16.7$\pm$0.6 29.6$\pm$1.3 16.7$\pm$0.4 BDKMR (本文) 16.9$\pm$0.5 30.1$\pm$1.1 16.8$\pm$0.6 30 FRCN+ft-full[33] 7.4$\pm$1.1 13.1$\pm$2.1 7.4$\pm$1.0 TFA[14] 12.1$\pm$0.4 22.0$\pm$0.7 12.0$\pm$0.5 DeFRCN[39] 21.0$\pm$0.4 36.7$\pm$0.8 21.4$\pm$0.4 BDKMR (本文) 19.7$\pm$0.5 34.4$\pm$1.0 20.2$\pm$0.4
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表 5 基于COCO数据集的分类器消融实验结果(%)
Table 5 Ablative experimental results in the classifiers on COCO dataset (%)
分类器 nAP nAP50 BMCR PWE WN 1 2 3 5 10 30 1 2 3 5 10 30 $\times$ $\times$ $\times$ 7.3 10.9 12.5 14.1 17.1 20.0 13.4 20.9 23.8 27.7 32.9 37.0 $\times$ $\times$ $\checkmark$ 8.0 11.4 12.7 14.1 16.9 19.7 14.4 21.5 24.1 27.4 32.6 36.6 $\checkmark$ $\times$ $\checkmark$ 8.4 11.7 13.0 14.3 17.2 19.8 15.3 22.0 24.9 28.0 32.9 36.7 $\times$ $\checkmark$ $\checkmark$ 8.3 11.6 12.6 13.9 16.8 19.4 15.2 22.0 24.1 27.4 32.5 36.3 $\checkmark$ $\checkmark$ $\checkmark$ 8.9 12.1 12.9 14.3 17.0 19.5 17.0 23.1 25.1 28.2 32.9 36.2
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表 6 基于COCO数据集的特征提取器消融实验结果(%)
Table 6 Ablative experimental results in the feature extractor on COCO dataset (%)
BDKMR FE nAP nAP50 微调 DBC 1 2 3 5 10 30 1 2 3 5 10 30 $\times$ $\checkmark$ $\times$ 6.5 11.8 13.4 15.3 18.6 22.5 11.0 20.6 24.3 28.4 34.6 39.9 $\checkmark$ $\checkmark$ $\times$ 8.7 12.1 13.0 14.7 17.4 20.8 15.8 22.4 24.7 28.7 33.2 37.7 $\checkmark$ $\checkmark$ $\checkmark$ 9.8 13.3 13.8 15.5 18.1 21.6 17.9 24.7 26.3 30.4 34.4 39.0
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表 7 COCO数据集上不同$\alpha $值下的实验结果(%)
Table 7 Experimental results for different $\alpha $ values on COCO dataset (%)
$\alpha$ 样本数 合计$\Delta$ 1 2 3 10 0 8.0 11.4 12.7 16.9 — 1 8.1 11.5 12.7 17.0 +0.3 10 8.3 11.5 12.6 16.9 +0.3 30 8.4 11.8 13.0 16.9 +1.1 50 8.4 11.7 13.0 17.2 +1.3 70 8.3 11.4 12.8 17.0 +0.5 90 8.3 11.6 13.0 17.0 +0.9 5 000 7.3 10.3 11.5 15.7 −4.2
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表 8 COCO数据集上不同$\beta $值下的实验结果(%)
Table 8 Experimental results for different $\beta $values on COCO dataset (%)
$\beta$ 样本数 合计$\Delta$ 1 2 3 10 0.0 8.4 11.7 13.0 17.2 — 0.1 8.7 11.9 13.1 17.0 +0.4 0.2 8.9 12.1 12.9 17.0 +0.6 0.4 9.1 11.9 12.7 16.7 +0.1 0.8 9.0 11.7 12.4 16.3 −0.9
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表 9 COCO数据集上不同p值下的实验结果(%)
Table 9 Experimental results for different $p $ values on COCO dataset (%)
$p$ 样本数 合计$\Delta$ 1 2 3 10 固定特征提取器参数 — 7.3 10.9 12.5 17.1 — 1 8.7 11.7 12.8 17.0 +2.4 2 8.9 12.1 12.9 17.0 +3.1 3 8.8 12.0 13.0 16.7 +2.7 4 8.9 12.1 12.8 16.6 +2.4 5 8.7 12.1 12.8 16.6 +2.4 更新特征提取器参数 — 6.5 11.8 13.4 18.6 — 1 9.6 13.0 13.8 18.0 +4.1 2 9.8 13.3 13.8 18.1 +4.7 3 10.0 13.2 14.1 18.0 +5.0 4 9.2 13.0 13.5 17.5 +2.9 5 9.1 12.7 13.8 17.6 +2.8
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表 10 COCO数据集上两种PWE实现方式的比较(%)
Table 10 Comparisons of two implementations of PWE on COCO dataset (%)
APWE CPWE $\gamma$ 样本数 合计$\Delta$ 1 2 3 10 $\times$ $\times$ — 8.0 11.4 12.7 16.9 — $\checkmark$ $\times$ — 8.3 11.6 12.6 16.8 +0.3 $\times$ $\checkmark$ 1.00 8.3 11.2 12.2 16.2 −1.1 $\times$ $\checkmark$ 0.95 8.4 11.6 12.6 16.7 +0.3 $\times$ $\checkmark$ 0.90 8.2 11.6 12.7 16.8 +0.3 $\times$ $\checkmark$ 0.85 8.2 11.5 12.7 17.0 +0.4 $\times$ $\checkmark$ 0.80 8.1 11.6 12.8 16.8 +0.3
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表 11 将BDKMR插入其他基线方法的模型性能(%)
Table 11 Model performance by integrating BDKMR into other baseline methods (%)
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表 12 基于VOC数据集新类分割1的全部实验结果(%)
Table 12 Full experimental results on the novel set 1 of VOC dataset (%)
方法 1-shot 2-shot 3-shot 5-shot 10-shot mAP bAP nAP mAP bAP nAP mAP bAP nAP mAP bAP nAP mAP bAP nAP MPSR[65] 56.8 — 41.7 60.4 — 42.5 62.8 — 51.4 66.1 — 55.2 69.0 — 61.8 TFA w/ fc[14] 69.3 80.2 36.8 66.9 79.5 29.1 70.3 79.2 43.6 73.4 79.2 55.7 73.2 78.6 57.0 TFA w/ cosine[14] 69.7 79.6 39.8 68.2 78.9 36.1 70.5 79.1 44.7 73.4 79.3 55.7 72.8 78.4 56.0 Retentive R-CNN[45] 71.3 — 42.4 72.3 — 45.8 72.1 — 45.9 74.0 — 53.7 74.6 — 56.1 DeFRCN*[39] 72.4 78.1 55.1 73.1 76.9 61.9 73.5 76.4 64.9 74.3 77.2 65.8 74.1 76.7 66.2 BDKMR (本文) 73.3 78.3 58.3 74.0 77.0 65.1 74.7 77.1 67.2 75.0 77.4 67.8 74.5 77.1 66.6
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