  In this paper, we study the problem of recognizing compositional
attribute-object concepts within the zero-shot learning (ZSL) framework. We
propose an episode-based cross-attention (EpiCA) network which combines merits
of cross-attention mechanism and episode-based training strategy to recognize
novel compositional concepts. Firstly, EpiCA bases on cross-attention to
correlate concept-visual information and utilizes the gated pooling layer to
build contextualized representations for both images and concepts. The updated
representations are used for a more in-depth multi-modal relevance calculation
for concept recognition. Secondly, a two-phase episode training strategy,
especially the transductive phase, is adopted to utilize unlabeled test
examples to alleviate the low-resource learning problem. Experiments on two
widely-used zero-shot compositional learning (ZSCL) benchmarks have
demonstrated the effectiveness of the model compared with recent approaches on
both conventional and generalized ZSCL settings.
