Improving Object Detection in Art Images Using Only Style Transfer

Despite recent advances in object detection using deep learning neural networks, these neural networks still struggle to identify objects in art images such as paintings and drawings. This challenge is known as the cross depiction problem and it stems in part from the tendency of neural networks to prioritize identification of an object’s texture over its shape. In this paper we propose and evaluate a process for training neural networks to localize objects – specifically people – in art images. We generate a large dataset for training and validation by modifying the images in the COCO dataset using AdaIn style transfer. This dataset is used to fine-tune a Faster R-CNN object detection network, which is then tested on the existing People-Art testing dataset. The result is a significant improvement on the state of the art and a new way forward for creating datasets to train neural networks to process art images.

虽然最近深度学习在目标检测领域有了长足发展,但是这些网络在艺术作品如画作等数据上的表现不佳。这个问题主要是因为神经网络倾向于通过目标的纹理而非形状进行推断。在本文中我们提出并且验证一种训练检测器的流程,这个流程训练的是对于艺术作品中的人物。我们使用AdaIn风格迁移将COCO数据集构建成一个庞大的数据集,然后在People-Art testing数据集上进行测试。结果显示我们的方法有效地提高了现有检测器在艺术作品上的检测表现。


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