论文标题
从野外图像中学习时尚兼容性
Learning Fashion Compatibility from In-the-wild Images
论文作者
论文摘要
互补的时尚推荐旨在识别来自不同类别的项目（例如衬衫，鞋类等），这些项目“很好地在一起”是一件服装。大多数现有方法使用包含手动策划的兼容项目组合的标记的Outfit数据集学习此任务的表示形式。在这项工作中，我们建议通过利用人们经常穿兼容服装的事实来学习从野外街头时尚图像中的兼容性预测。我们制定了我们的借口任务，以使同一个人所穿的不同物品的表示形式与其他人所穿的物品相比更接近。此外，为了减少推理期间野外图像和目录图像之间的域间隙，我们引入了对抗性损失，以最大程度地减少两个域之间特征分布的差异。我们对两个流行的时尚兼容性基准进行了实验 - 多视和多视频连接服装，并且表现优于现有的自我监督方法，在跨数据库环境中尤其重要，在跨数据库设置中，训练和测试图像来自不同来源。
Complementary fashion recommendation aims at identifying items from different categories (e.g. shirt, footwear, etc.) that "go well together" as an outfit. Most existing approaches learn representation for this task using labeled outfit datasets containing manually curated compatible item combinations. In this work, we propose to learn representations for compatibility prediction from in-the-wild street fashion images through self-supervised learning by leveraging the fact that people often wear compatible outfits. Our pretext task is formulated such that the representations of different items worn by the same person are closer compared to those worn by other people. Additionally, to reduce the domain gap between in-the-wild and catalog images during inference, we introduce an adversarial loss that minimizes the difference in feature distribution between the two domains. We conduct our experiments on two popular fashion compatibility benchmarks - Polyvore and Polyvore-Disjoint outfits, and outperform existing self-supervised approaches, particularly significant in cross-dataset setting where training and testing images are from different sources.
