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Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition | | Xiao Liu
; Jiang Wang
; Shilei Wen
; Errui Ding
; Yuanqing Lin
; | | Date: |
20 May 2016 | | 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. | | Source: | arXiv, 1605.6217 | | Services: | Forum | Review | PDF | Favorites | |
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