Maria: A Visual Experience Powered Conversational Agent

TL;DR

Maria is a novel visual experience-powered conversational agent that retrieves images from a large-scale index and generates informed responses grounded in visual knowledge, advancing open-ended image-grounded dialogue.

Contribution

This work introduces Maria, a fully open-ended image-grounded conversational model with a retrieval-based visual knowledge component, unlike prior models relying on paired dialog-image data.

Findings

Maria outperforms state-of-the-art methods on automatic metrics.

Maria generates responses with visual commonsense of the physical world.

Extensive experiments validate the effectiveness of Maria's approach.

Abstract

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.