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Are there any good ways of simultaneously incorporating object detection with speech recognition? For example, if you want to identify whether an animal is a dog or cat, we can obviously use visual features (e.g. YOLO, CNNs, etc.). But how would you incorporate speech and sound in this model?

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Check out this paper. It deals with the problem of mixing input modalities! Here's the abstract.

This paper presents a novel model for multimodal learning based on gated neural networks. The Gated Multimodal Unit (GMU) model is intended to be used as an internal unit in a neural network architecture whose purpose is to find an intermediate representation based on a combination of data from different modalities. The GMU learns to decide how modalities influence the activation of the unit using multiplicative gates. It was evaluated on a multilabel scenario for genre classification of movies using the plot and the poster. The GMU improved the macro f-score performance of single-modality approaches and outperformed other fusion strategies, including mixture of experts models. Along with this work, the MM-IMDb dataset is released which, to the best of our knowledge, is the largest publicly available multimodal dataset for genre prediction on movies.

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