Maria: A Visual Experience Powered Conversational Agent
This addresses the challenge of enabling conversational agents to exhibit human-like visual intelligence for applications in AI assistants, though it is incremental by extending existing multimodal dialog models to an open-ended scenario.
The paper tackles the problem of image-grounded conversation in an open-ended setting without paired dialog and image data, presenting Maria, a conversational agent that retrieves images and uses visual knowledge to generate responses, achieving state-of-the-art performance on automatic metrics and human evaluation.
Arguably, the visual perception of conversational agents to the physical world is a key way for them to exhibit the human-like intelligence. Image-grounded conversation is thus proposed to address this challenge. Existing works focus on exploring the multimodal dialog models that ground the conversation on a given image. In this paper, we take a step further to study image-grounded conversation under a fully open-ended setting where no paired dialog and image are assumed available. Specifically, we present Maria, a neural conversation agent powered by the visual world experiences which are retrieved from a large-scale image index. Maria consists of three flexible components, i.e., text-to-image retriever, visual concept detector and visual-knowledge-grounded response generator. The retriever aims to retrieve a correlated image to the dialog from an image index, while the visual concept detector extracts rich visual knowledge from the image. Then, the response generator is grounded on the extracted visual knowledge and dialog context to generate the target response. Extensive experiments demonstrate Maria outperforms previous state-of-the-art methods on automatic metrics and human evaluation, and can generate informative responses that have some visual commonsense of the physical world.