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Describing visual data into natural language is a very challenging task, atthe intersection of computer vision, natural language processing and machinelearning. Language goes well beyond the description of physical objects andtheir interactions and can convey the same abstract idea in many ways. It isboth about content at the highest semantic level as well as about fluent form.Here we propose an approach to describe videos in natural language by reachinga consensus among multiple encoder-decoder networks. Finding such a consensuallinguistic description, which shares common properties with a larger group, hasa better chance to convey the correct meaning. We propose and train severalnetwork architectures and use different types of image, audio and videofeatures. Each model produces its own description of the input video and thebest one is chosen through an efficient, two-phase consensus process. Wedemonstrate the strength of our approach by obtaining state of the art resultson the challenging MSR-VTT dataset.

Mining for meaning: from vision to language through multiple networks consensus