Embedding images and sentences in a common space with a recurrent capsule network

Francis, Danny; Huet, Benoit; Merialdo, Bernard
CBMI 2018, International Conference on Content-Based
Multimedia Indexing, 4-6 September 2018, La Rochelle, France

Associating texts and images is an easy and intuitive task for a human being, but it raises some issues if we want that task to be accomplished by a computer. Among these issues, there is the problem of finding a common representation for images and sentences. Based on recent research about capsule networks, we define a novel model to tackle that issue. This model is trained and compared to other recent models on the Flickr8k database on
Image Retrieval and Image Annotation (or Sentence Retrieval) tasks. We propose a new recurrent architecture inspired from capsule networks to replace the traditional LSTM/GRU and show how it leads to improved performances. Moreover, we show that
the interest of our model goes beyond its performances and includes its intrinsic characteristics, which can explain why it performs particularly well on the Image Annotation task. In addition, we propose a routing procedure between capsules which
is fully learned during the training of our model.

La Rochelle
Data Science
Eurecom Ref:
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