How does HERO's cross-modal fusion efficiency compare to other multimodal architectures when processing high-r

Abstract

Abstract: In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respective

Research Question

How does HERO's cross-modal fusion efficiency compare to other multimodal architectures when processing high-resolution video frames with corresponding subtitles?

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