AAAI Publications, Thirty-First AAAI Conference on Artificial Intelligence

Font Size: 
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
Xiao Liu, Jiang Wang, Shilei Wen, Errui Ding, Yuanqing Lin

Last modified: 2017-02-12

Abstract


A key challenge in fine-grained recognition is how to find and represent discriminative local regions.Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately find multiple distinctive regions.In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions.By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm. The attribute labeling requirement of the scheme is more amenable than the accurate part location annotation required by traditional part-based fine-grained recognition methods.Experimental results on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme on both fine-grained recognition and attribute recognition.

Keywords


attention, reinforcement learning, fine-grained

Full Text: PDF