Zero-Shot Compositional Concept Learning
Guangyue Xu, Parisa Kordjamshidi, Joyce Yue Chai · 2021
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 conceptvisual information and utilizes the gated pooling layer to build contextualized representations for both images and concepts.The updated representations are used for a more indepth 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 widelyused zero-shot compositional learning (ZSCL) benchmarks have demonstrated the effectiveness of the model compared with recent approaches on both conventional and generalized ZSCL settings.