  Zero-shot learning (ZSL) aims to recognize objects from unseen classes, where
the kernel problem is to transfer knowledge from seen classes to unseen classes
by establishing appropriate mappings between visual and semantic features. The
knowledge transfer in many existing works is limited mainly due to the facts
that 1) the widely used visual features are global ones but not totally
consistent with semantic attributes; 2) only one mapping is learned in existing
works, which is not able to effectively model diverse visual-semantic
relations; 3) the bias problem in the generalized ZSL (GZSL) could not be
effectively handled. In this paper, we propose two techniques to alleviate
these limitations. Firstly, we propose a Semantic-diversity transfer Network
(SetNet) addressing the first two limitations, where 1) a multiple-attention
architecture and a diversity regularizer are proposed to learn multiple local
visual features that are more consistent with semantic attributes and 2) a
projector ensemble that geometrically takes diverse local features as inputs is
proposed to model visual-semantic relations from diverse local perspectives.
Secondly, we propose an inner disagreement based domain detection module (ID3M)
for GZSL to alleviate the third limitation, which picks out unseen-class data
before class-level classification. Due to the absence of unseen-class data in
training stage, ID3M employs a novel self-contained training scheme and detects
out unseen-class data based on a designed inner disagreement criterion.
Experimental results on three public datasets demonstrate that the proposed
SetNet with the explored ID3M achieves a significant improvement against $30$
state-of-the-art methods.
