This paper extends fully-convolutional neural networks (FCN) for the clothing parsing problem. Clothing parsing requires higher-level knowledge on clothing semantics and contextual cues to disambiguate fine-grained categories. We extend FCN architecture with a side-branch network which we refer outfit encoder to predict a consistent set of clothing labels to encourage combinatorial preference, and with conditional random field (CRF) to explicitly consider coherent label assignment to the given image. The empirical results using Fashionista and CFPD datasets show that our model achieves state-of-the-art performance in clothing parsing, without additional supervision during training. We also study the qualitative influence of annotation on the current clothing parsing benchmarks, with our Web-based tool for multi-scale pixel-wise annotation and manual refinement effort to the Fashionista dataset. Finally, we show that the image representation of the outfit encoder is useful for dress-up image retrieval application.

基于全卷积神经网络，在衣服解析问题中引入了服装套装编码器和条件随机场，能够有效处理衣服语义和上下文信息。在Fashionista和CFPD数据集中取得了最佳的解析效果，并利用基于Web的多尺度像素级注释工具对当前解析基准测试的影响进行了定量研究，同时证明了服装套装编码器的图像表示在着装图像检索应用中具有实用价值。

审视装扮以解析服装