Continual Learning about Objects in the Wild: An Interactive Approach

Abstract

We introduce a mixed-reality, interactive approach for continually learning to recognize an open-ended set of objects in a user's surrounding environment. The proposed approach leverages the multimodal sensing, interaction, and rendering affordances of a mixed-reality headset, and enables users to label nearby objects via speech, gaze, and gestures. Image views of each labeled object are automatically captured from varying viewpoints over time, as the user goes about their everyday tasks. The labels provided by the user can be propagated forward and backwards in time and paired with the collected views to update an object recognition model, in order to continually adapt it to the user's specific objects and environment. We review key challenges for the proposed interactive continual learning approach, present details of an end-to-end system implementation, and report on results and lessons learned from an initial, exploratory case study using the system.