BBAuthors: Wang, Xuesong; Advisor: -; Participants: Li, Qianyu; Gong, Ping; Cheng, Yuhu (2019)
Since the learning of attribute classifiers is independent of the learning of object classifier in zero-shot learning, it is difficult to guarantee that the learned attribute classifiers are optimal for the subsequent object recognition tasks. Therefore, a novel zero-shot learning method based on multitask extended attribute groups (MTEAGs) is proposed by using the multitask learning framework and grouping idea. First, we used an unsupervised clustering method to group the attributes and object classes of training images. Then, based on the obtained attribute and class groups, we constructed the group-based attribute/object classifier collaborative learning model where the class group... 
