Abstract: Due to the rapid development of the Internet and social media,cross-modal retrieval has attracted extensive attention.The purpose of cross-modal retrieval is to achieve flexible retrieval of different modalities.The heterogeneity gap between diffe-rent modal suggests that the similarity of different modal features cannot be calculated directly,making it difficult to improve the accuracy of cross-modal retrieval.This paper proposes an image-text cross-modal retrieval method for dual gating-residual feature fusion(DGRFF),to narrow the heterogeneity gap between the image and text.By designing gating features and residual features to fusion the features of image modality and text modality,this method can gain more effective feature information from the opposite modality,making semantic feature information more comprehensive.At the same time,the adversarial loss is adopted to align the feature distribution of the two modalities,to maintain the modality invariance of the fusion feature and obtain a more recogni-zable feature representation in the public potential space.Finally,the model is trained by combining label prediction loss,cross-modal similarity loss and adversarial loss.Experiments on Wikipedia and Pascal Sentence datasets show that DGRFF performs well on cross-modal retrieval tasks.

Key words: Cross-modal retrieval, Heterogeneity gap, Gating features, Residual features, Feature fusion