Learning DALTS for cross-modal retrieval

• 作者: Wenmin Wang Zheng Yu
• 来源期刊: 智能技术学报
• 学科: 社会科学
• 关键词: DALTS MSCOCO
• 年，卷（期）: 2019,（1）
• 页码范围: 9-16
• 页数: 8页
• 分类号: G

摘要

Cross-modal retrieval has been recently proposed to find an appropriate subspace,where the similarity across different modalities such as image and text can be directly measured.In this study,different from most existing works,the authors propose a novel model for cross-modal retrieval based on a domain-adaptive limited text space(DALTS)rather than a common space or an image space.Experimental results on three widely used datasets,Flickr8K,Flickr30K and Microsoft Common Objects in Context(MSCOCO),show that the proposed method,dubbed DALTS,is able to learn superior text space features which can effectively capture the necessary information for cross-modal retrieval.Meanwhile,DALTS achieves promising improvements in accuracy for cross-modal retrieval compared with the current state-of-the-art methods.