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摘要:
小样本图像识别是计算机视觉中的关键问题。针对小样本情况下度量学习方法的类别原型不准确、泛化能力差问题，本文采用以下措施来提高小样本图像识别准确率：第一、为减缓样本稀缺问题，利用掩膜自编码器进行图像扩充，提高样本复杂度。第二、设计多尺度注意力模块，突出类别相关特征，解决类别原型偏差大的问题。第三、提出领域自适应模块，引入间隔损失函数，优化嵌入函数的表征能力，实现新类样本的精确表征，增强模型的泛化能力。通过在多个公开数据集上进行实验表明，本文方法可以有效提升小样本图像识别准确率。
Abstract:Learning with limited data is a challenging field for computer visual recognition. Prototypes calculated by the metric learning method are inaccurate when samples are limited. In addition, the generalization ability of the model is poor. To improve the performance of few-shot image classification, the following measures are adopted. Firstly, to tackle the problem of limited samples, the masked autoencoder is used to enhance data. Secondly, prototypes are calculated by task-specific features, which are obtained by the multi-scale attention mechanism. The attention mechanism makes prototypes more accurate. Thirdly, the domain adaptation module is added with a margin loss function. The margin loss pushes different prototypes away from each other in the feature space. Sufficient margin space improves the generalization performance of the method. The experimental results show the proposed method achieves better performance on few-shot classification.
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图 2 多尺度注意力与领域自适应小样本分类模型结构
Figure 2. The framework of few-shot image classification via multi-scale attention and domain adaptation
图 5 领域自适应模块工作原理。(a) 模型训练阶段；(b) 模型测试阶段
Figure 5. Operating principle of the domain adaptation module. (a) Model training process; (b) Model testing process
图 6 MiniImageNet数据集模型收敛。(a) 5-way 1-shot；(b) 5-way 5-shot
Figure 6. Model convergence on the miniImageNet dataset. (a) 5-way 1-shot；(b) 5-way 5-shot
图 7 MiniImageNet数据集模型损失。 (a) 5-way 1-shot；(b) 5-way 5-shot
Figure 7. Model loss on the miniImageNet dataset. (a) 5-way 1-shot；(b) 5-way 5-shot
图 8 卷积神经网络视觉特征可视化
Figure 8. Visualization of features based on convolutional neural network
图 9 MiniImageNet中5类图像特征向量可视化。(a) Baseline方法；(b) 本文方法
Figure 9. Visualization of image features in five classes of miniImageNet. (a) Baseline method; (b) MADA method
图 11 领域自适应模型性能分析。(a) 5-way 1-shot ；(b) 5-way 5-shot
Figure 11. Domain adaptation module analysis. (a) 5-way 1-shot; (b) 5-way 5-shot
表 1 骨架网络的模型结构
Table 1. Structure of the backbone
模型结构 输出尺寸 ResNet-12 Conv4 卷积层1 42$ \times $42 [3×3，64] × 3 [3×3，64] 卷积层2 21$ \times $21 [3×3，160] × 3 [3×3，64] 卷积层3 10$ \times $10 [3×3，320] × 3 [3×3，64] 卷积层4 5$ \times $5 [3×3，640] × 3 [3×3，64] 池化层 1$ \times $1 5×5 Pool 5×5 Pool 参数量 50 MB 0.46 MB 
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表 2 MiniImageNet数据集置信度95%小样本分类准确率 (episodes为10000)
Table 2. Few-shot classification accuracies with 95 confidence interval on the miniImageNet dataset (the number of episodes is 10000)
模型 骨架网络 5-way 1-shot 5-way 5-shot Matching Net [12] Conv4 43.56±0.84 55.31±0.37 Proto Net [13] Conv4 49.42±0.78 68.20±0.66 Relation Net [21] Conv4 50.44±0.82 65.32±0.70 MAML [9] Conv4 48.70±1.84 63.11±0.92 DN4 [14] Conv4 51.24±0.74 71.02±0.64 DSN [23] Conv4 51.78±0.96 68.99±0.69 BOIL [25] Conv4 49.61±0.16 66.45±0.37 MADA(ours) Conv4 55.27±0.20 72.12±0.16 Matching Net [12] ResNet-12 65.64±0.20 78.73±0.15 Proto Net [13] ResNet-12 60.37±0.83 78.02±0.75 DN4 [14] ResNet-12 54.37±0.36 74.44±0.29 DSN [23] ResNet-12 62.64±0.66 78.73±0.45 SNAIL [22] ResNet-12 55.71±0.99 68.88±0.92 CTM [24] ResNet-12 64.12±0.82 80.51±0.14 MADA(ours) ResNet-12 67.45±0.20 82.77±0.13
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表 3 TieredImageNet数据集置信度95%小样本分类准确率 (episodes为10000)
Table 3. Few-shot classification accuracies with 95 confidence interval on the tieredImageNet dataset (the number of episodes is 10000)
模型 骨架网络 5-way 1-shot 5-way 5-shot Matching Net [12] ResNet-12 68.50±0.92 80.60±0.71 Proto Net [13] ResNet-12 65.65±0.92 83.40±0.65 MetaOpt Net [26] ResNet-12 65.99±0.72 81.56±0.53 TPN [27] ResNet-12 59.91±0.94 73.30±0.75 CTM [24] ResNet-12 68.41±0.39 84.28±1.74 LEO [10] ResNet-12 66.63±0.05 81.44±0.09 MADA(ours) ResNet-12 70.67±0.22 85.10±0.15
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表 4 CUB数据集置信度95%小样本分类准确率 (episodes为10000)
Table 4. Few-shot classification accuracies with 95 confidence interval on the CUB dataset (the number of episodes is 10000)
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表 5 在miniImageNet数据集上置信度95%小样本分类的消融实验 (episodes为10000)
Table 5. Ablation study of few-shot classification accuracies with 95 confidence interval on the miniImageNet (the number of episodes is 10000)
网络 MA DA DE 5-way 1-shot 5-way 5-shot Baseline × × × 60.37±0.83 78.02±0.75 MA √ × × 65.84±0.23 81.94±0.34 MADA √ √ × 67.21±0.18 82.41±0.48 MADA+ √ √ √ 67.45±0.20 82.77±0.13
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