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Several computer vision applications such as person search or online fashionrely on human description. The use of instance-level human parsing (HP) istherefore relevant since it localizes semantic attributes and body parts withina person. But how to characterize these attributes? To our knowledge, only somesingle-HP datasets describe attributes with some color, size and/or patterncharacteristics. There is a lack of dataset for multi-HP in the wild with suchcharacteristics. In this article, we propose the dataset CCIHP based on themulti-HP dataset CIHP, with 20 new labels covering these 3 kinds ofcharacteristics. In addition, we propose HPTR, a new bottom-up multi-taskmethod based on transformers as a fast and scalable baseline. It is the fastestmethod of multi-HP state of the art while having precision comparable to themost precise bottom-up method. We hope this will encourage research for fastand accurate methods of precise human descriptions.

Describe Me If You Can! Characterized Instance-Level Human Parsing