Image Tagging by Joint Deep Visual-Semantic Propagation

Yuexin Ma, Xinge Zhu, Yujing Sun, Bingzheng Yan

Abstract: Image tagging has attracted much research interest due to its wide applications. Many existing methods have gained impressive results, however, they have two main limitations: (1) only focus on tagging images, but ignore the tags’ influences on visual feature modeling. (2) model the tag correlation without considering visual contents of image. In this paper, we propose a joint visual-semantic propagation model (JVSP) to address these two issues. First, we leverage a joint visual-semantic modeling to harvest integrated features which can accurately reflect the relationship between tags and image regions. Second, we introduce a visual-guided LSTM to capture the co-occurrence relation of the tags. Third, we also design a diversity loss to enforce that our model learns to focus on different regions. Experimental results on three challenging datasets demonstrate that our proposed method leads to significant performance gains over existing methods.