论文标题
学习时尚服装的颜色兼容性
Learning Color Compatibility in Fashion Outfits
论文作者
论文摘要
颜色兼容性对于评估时尚服装的兼容性很重要，但在先前的研究中被忽略了。我们将这个重要的问题带给研究人员的注意力，并提出兼容性学习框架作为解决各种时尚任务的解决方案。该框架包括一种新颖的方式来建模服装兼容性和创新学习方案。具体而言，我们将服装建模为图形，并提出一种新颖的图形结构，以更好地利用图形神经网络的功能。然后，我们利用地面真实标签和伪标签以弱监督的方式训练兼容性模型。扩展的实验结果可以验证颜色兼容性仅具有框架的有效性的重要性。仅使用颜色信息，我们的模型的性能已经与以前使用深层图像功能的方法相媲美。我们结合上述贡献的完整模型为时尚兼容性预测带来了新的最先进。
Color compatibility is important for evaluating the compatibility of a fashion outfit, yet it was neglected in previous studies. We bring this important problem to researchers' attention and present a compatibility learning framework as solution to various fashion tasks. The framework consists of a novel way to model outfit compatibility and an innovative learning scheme. Specifically, we model the outfits as graphs and propose a novel graph construction to better utilize the power of graph neural networks. Then we utilize both ground-truth labels and pseudo labels to train the compatibility model in a weakly-supervised manner.Extensive experimental results verify the importance of color compatibility alone with the effectiveness of our framework. With color information alone, our model's performance is already comparable to previous methods that use deep image features. Our full model combining the aforementioned contributions set the new state-of-the-art in fashion compatibility prediction.
