用于StyleGAN图像自动分割的自监督单样本学习
Self-Supervised One-Shot Learning for Automatic Segmentation of StyleGAN Images
AI总结：
提出一种基于GAN隐藏特征和自监督对比聚类的单样本分割框架，在五个基准上性能优于半监督基线且推理速度提升4.5倍，并成功应用于BagGAN行李X射线威胁检测。
摘要：
我们提出了一个用于对StyleGAN生成的合成图像进行自动单样本分割的框架。我们的框架基于这样一个观察：GAN生成器中的多尺度隐藏特征包含有用的语义信息，可用于对生成图像进行自动的实时分割。利用这些特征，我们的框架通过一种自监督对比聚类算法学习对合成图像进行分割，该算法将隐藏特征投影到一个紧凑空间中以进行逐像素分类。该对比学习器基于一种新颖的数据增强策略和一种像素级交换预测损失，从而能更快地学习用于单样本分割的特征向量。我们在五个标准基准上测试了我们的实现，其分割性能不仅以平均1.02%的wIoU优势优于半监督基线，而且将推理速度提高了4.5倍。最后，我们还展示了使用所提出的单样本学习器在实现BagGAN中的结果，这是一个用于生成带标注的合成行李X射线扫描以进行威胁检测的框架。该框架在PIDRay行李基准上进行了训练和测试，其性能可与基于手动标注的基线分割器相媲美。
英文摘要：
We propose a framework for the automatic one-shot segmentation of synthetic images generated by a StyleGAN. Our framework is based on the observation that the multi-scale hidden features in the GAN generator hold useful semantic information that can be utilized for automatic on-the-fly segmentation of the generated images. Using these features, our framework learns to segment synthetic images using a self-supervised contrastive clustering algorithm that projects the hidden features into a compact space for per-pixel classification. This contrastive learner is based on using a novel data augmentation strategy and a pixel-wise swapped prediction loss that leads to faster learning of the feature vectors for one-shot segmentation. We have tested our implementation on five standard benchmarks to yield a segmentation performance that not only outperforms the semi-supervised baselines by an average wIoU margin of 1.02 % but also improves the inference speeds by a factor of 4.5. Finally, we also show the results of using the proposed one-shot learner in implementing BagGAN, a framework for producing annotated synthetic baggage X-ray scans for threat detection. This framework was trained and tested on the PIDRay baggage benchmark to yield a performance comparable to its baseline segmenter based on manual annotations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
