论文标题
在深度卷积神经网络中提高引导注意力和人类知识插入
Towards Better Guided Attention and Human Knowledge Insertion in Deep Convolutional Neural Networks
论文作者
论文摘要
注意分支网络（ABN）已被证明可以同时提供视觉解释并改善深卷卷神经网络（CNN）的性能。在这项工作中，我们介绍了多尺度注意分支网络（MSABN），从而增强了生成的注意图的分辨率，并提高了性能。我们在基准图像识别和细粒识别数据集上评估了MSABN，在该数据集中我们观察到MSABN优于ABN和基线模型。我们还利用注意图引入了一种新的数据增强策略，以关注对象的边界框注释的形式结合人类知识。我们表明，即使有数量有限的编辑样本，这种策略也可以实现可观的性能增长。
Attention Branch Networks (ABNs) have been shown to simultaneously provide visual explanation and improve the performance of deep convolutional neural networks (CNNs). In this work, we introduce Multi-Scale Attention Branch Networks (MSABN), which enhance the resolution of the generated attention maps, and improve the performance. We evaluate MSABN on benchmark image recognition and fine-grained recognition datasets where we observe MSABN outperforms ABN and baseline models. We also introduce a new data augmentation strategy utilizing the attention maps to incorporate human knowledge in the form of bounding box annotations of the objects of interest. We show that even with a limited number of edited samples, a significant performance gain can be achieved with this strategy.
