Dialog-based Interactive Image Retrieval – Arxiv Vanity

In this work, we propose a new approach to interactive image search by introducing a novel form of user feedback based on natural language. The proposed approach, which we call dialog-based interactive retrieval, enables users to directly express in natural language, what the most prominent visual attributes of the image they have in mind are, improving image search results and allowing for a more natural human-computer interaction. We formulate the task as a reinforcement learning (RL) problem, and train a dialog system that takes natural language responses as user input, and produces retrieved images as output. We train this system by directly optimizing the rank of the target image, which is a non-differentiable objective. To avoid the cumbersome, inefficient, and costly process of collecting and annotating human-machine dialogs as the system learns, we utilize a model-based reinforcement learning approach by training a user simulator based on human-written relative descriptions.