Reading Recognition in the Wild

To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able to keep a record of the user's interactions with the world, including during reading. In this paper, we introduce a new task of reading recognition to determine when the user is reading. We first introduce the first-of-its-kind large-scale multimodal Reading in the Wild dataset, containing 100 hours of reading and non-reading videos in diverse and realistic scenarios. We then identify three modalities (egocentric RGB, eye gaze, head pose) that can be used to solve the task, and present a flexible transformer model that performs the task using these modalities, either individually or combined. We show that these modalities are relevant and complementary to the task, and investigate how to efficiently and effectively encode each modality. Additionally, we show the usefulness of this dataset towards classifying types of reading, extending current reading understanding studies conducted in constrained settings to larger scale, diversity and realism. Code, model, and data will be public. Figure 1: Am I reading? The left figure shows a timeline as the user navigates the world. We aim to solve the task of reading recognition to enable AI assistants in always-on wearables. We identify three modalities: eye gaze (in colored dot patterns), RGB crop around gaze (in red box), and inertial sensors performs the task to high accuracy (with Prediction and GT shown). Images from our Reading in the Wild dataset, which features 100 hours of diverse reading and non-reading activities in real-world settings, with examples shown in the right.