This dataset not only has four different views for each clothing item, but also provides 264 attributes for describing clothing appearance. We adopt a state-of-the-art deep learning method to present baseline results for the attribute prediction and clothing retrieval performance. We also evaluate the method on a more difficult setting, cross-view exact clothing item retrieval. This dataset can be used for further studies towards view-invariant clothing retrieval.

Data Collection

We collect the MVC dataset by crawling images from several online shopping websites, such as Amazon.com, Zap- pos.com or Shopbop.com. The challenge in constructing the dataset is to gather complete four different views (front, back, left, and right views) for each clothing item, as there may be only two or three views available for some clothes.

In current stage, the MVC dataset consists of 37,499 items and 161,638 clothing images, where most items have at least four views. Most of the image resolutions are 1920 ¡Á 2240 pixels, which thus offers sufficient details for various clothing related studies such as clothing attribute localization and type classification.