Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts

• URL: http://arxiv.org/abs/2007.03338v1
• Date: Tue, 7 Jul 2020 11:00:27 GMT
• Title: Diverse and Styled Image Captioning Using SVD-Based Mixture of Recurrent Experts
• Authors: Marzieh Heidari, Mehdi Ghatee, Ahmad Nickabadi, Arash Pourhasan Nezhad
• Abstract summary: A new captioning model is developed including an image encoder to extract the features, a mixture of recurrent networks to embed the set of extracted features to a set of words, and a sentence generator that combines the obtained words as a stylized sentence. We show that the proposed captioning model can generate a diverse and stylized image captions without the necessity of extra-labeling.
• Score: 5.859294565508523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With great advances in vision and natural language processing, the generation of image captions becomes a need. In a recent paper, Mathews, Xie and He [1], extended a new model to generate styled captions by separating semantics and style. In continuation of this work, here a new captioning model is developed including an image encoder to extract the features, a mixture of recurrent networks to embed the set of extracted features to a set of words, and a sentence generator that combines the obtained words as a stylized sentence. The resulted system that entitled as Mixture of Recurrent Experts (MoRE), uses a new training algorithm that derives singular value decomposition (SVD) from weighting matrices of Recurrent Neural Networks (RNNs) to increase the diversity of captions. Each decomposition step depends on a distinctive factor based on the number of RNNs in MoRE. Since the used sentence generator gives a stylized language corpus without paired images, our captioning model can do the same. Besides, the styled and diverse captions are extracted without training on a densely labeled or styled dataset. To validate this captioning model, we use Microsoft COCO which is a standard factual image caption dataset. We show that the proposed captioning model can generate a diverse and stylized image captions without the necessity of extra-labeling. The results also show better descriptions in terms of content accuracy.

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