  Zero-shot learning enables the model to recognize unseen categories with the
aid of auxiliary semantic information such as attributes. Current works
proposed to detect attributes from local image regions and align extracted
features with class-level semantics. In this paper, we find that the choice
between local and global features is not a zero-sum game, global features can
also contribute to the understanding of attributes. In addition, aligning
attribute features with class-level semantics ignores potential intra-class
attribute variation. To mitigate these disadvantages, we present Attribute
Localization and Revision Network in this paper. First, we design Attribute
Localization Module (ALM) to capture both local and global features from image
regions, a novel module called Scale Control Unit is incorporated to fuse
global and local representations. Second, we propose Attribute Revision Module
(ARM), which generates image-level semantics by revising the ground-truth value
of each attribute, compensating for performance degradation caused by ignoring
intra-class variation. Finally, the output of ALM will be aligned with revised
semantics produced by ARM to achieve the training process. Comprehensive
experimental results on three widely used benchmarks demonstrate the
effectiveness of our model in the zero-shot prediction task.
