Weakly-Supervised Online Hashing with Refined Pseudo Tags
Chen-Lu Ding, Xin Luo, Xiao-Ming Wu, Yu-Wei Zhan, Rui Li, Hui Zhang, Xin-Shun Xu · Proceedings of the 31st ACM International Conference on Information & Knowledge Management · 2022
With the rapid development of social media, various types of tags uploaded by social users are attached to the images. Compared to clean labels marked by experts, although user-provided tags are imperfect, e.g., wrong tags, reduplicative tags, or missing tags, they are more diverse, fine-grained, and informative. Currently, there exist several weakly-supervised hashing methods attempting to learn hash codes using tags as supervision. Although they could benefiting from the rich information contained in tags, most of them may defy the nature of social media data. In real scenarios, social media data appears in streaming fashion, but most weakly-supervised hashing methods are just batch-based which cannot effectively handle streaming data. To this end, only one weakly-supervised online hashing method has been proposed, but it is still far from enough to alleviate the negative effects of tags.