  Zero-shot learning (ZSL) aims to recognize novel classes through transferring
shared semantic knowledge (e.g., attributes) from seen classes to unseen
classes. Recently, attention-based methods have exhibited significant progress
which align visual features and attributes via a spatial attention mechanism.
However, these methods only explore visual-semantic relationship in the spatial
dimension, which can lead to classification ambiguity when different attributes
share similar attention regions, and semantic relationship between attributes
is rarely discussed. To alleviate the above problems, we propose a Dual
Relation Mining Network (DRMN) to enable more effective visual-semantic
interactions and learn semantic relationship among attributes for knowledge
transfer. Specifically, we introduce a Dual Attention Block (DAB) for
visual-semantic relationship mining, which enriches visual information by
multi-level feature fusion and conducts spatial attention for visual to
semantic embedding. Moreover, an attribute-guided channel attention is utilized
to decouple entangled semantic features. For semantic relationship modeling, we
utilize a Semantic Interaction Transformer (SIT) to enhance the generalization
of attribute representations among images. Additionally, a global
classification branch is introduced as a complement to human-defined semantic
attributes, and we then combine the results with attribute-based
classification. Extensive experiments demonstrate that the proposed DRMN leads
to new state-of-the-art performances on three standard ZSL benchmarks, i.e.,
CUB, SUN, and AwA2.
