The goal of this workshop is to foster research on the next generation of visual perception systems that reason over label spaces that go beyond a list of simple category names. Modern applications of computer vision require systems that understand a full spectrum of labels, from plain category names (“person” or “cat” ), over modifying descriptions using attributes, actions, functions or relations (“women with yellow handbag” , “parked cars” , or “edible item” ), to specific referring descriptions (“the man in the white hat walking next to the fire hydrant” ). Natural language is a promising direction not only to enable such complex label spaces, but also to train such models from multiple datasets with different, and potentially conflicting, label spaces. Besides an excellent list of invited speakers from both academia and industry, the workshop will present the results of the OmniLabel challenge, which we held with our newly collected benchmark dataset that subsumes generic object detection, open-vocabulary detection, and referring expression comprehension into one unified and challenging task.