A multi-task model for multi-attribute fashion recognition and retrieval

We present a multi-task model to perform multiple fashion attribute recognition and retrieval together. Although classical computer vision problems, they still lack good performance in multiple classes. In the fashion domain, the recognition task to capture the attributes of fashion clothing is always challenging, as well as the retrieval task to find similar items of fashion clothing. To handle the first challenge, we built a recognition model to parallelly and independently output a set of labels, each for a pre-defined single attribute notion. To handle the second challenge, we embedded the fashion clothing image into a feature vector, where different subspaces capture different notions of similarities. These two sub-tasks were then connected to be trained jointly in a multi-task learning framework. Their losses were also well aligned, combined, and optimized collectively.