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This paper Deep Visual-Semantic Alignments for Generating Image Descriptions on image captioning proposed a Multimodal Recurrent Neural Network architecture.

From my understanding, the multimodal RNN is essentially a language model conditioned on a given image vector.

Somehow I'm confused what "multimodal" refers to in that paper, how do I interpret this? Does it mean that since it is conditioned on an image vector, the language model becomes multimodal?

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Multimodal literally means "multiple modes", where vision, language, audio, lidar, etc data are each a different mode of information provided by the world. So you can call any model which deals with more than one mode of sensory input a multimodal model.

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  • $\begingroup$ Thanks, I mean I basically understand the concept of a multimodal distribution, in that paper the multimodal RNN model outputs a categorical probability every step (just like many other RNN models), so I'm confused how the name multimodal should be understood in that context. $\endgroup$ – dontloo Feb 12 at 6:39
  • $\begingroup$ @dontloo multimodal is an overloaded term. the other sense of the word is a distribution with multiple peaks. that is not what is meant in this paper. $\endgroup$ – shimao Feb 12 at 6:44

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