Research

Humans naturally perceive surrounding scenes by unifying sound and sight from a first-person view. Likewise, machines are advanced to approach human intelligence by learning with multisensory inputs from an egocentric perspective. In this paper, we explore the challenging egocentric audio-visual object localization task and observe that 1) egomotion commonly exists in first-person recordings, even within a short duration; 2) The out-of-view sound components can be created when wearers shift their attention. To address the first problem, we propose a geometryaware temporal aggregation module that handles the egomotion explicitly. The effect of egomotion is mitigated by estimating the temporal geometry transformation and exploiting it to update visual representations. Moreover, we propose a cascaded feature enhancement module to overcome the second issue. It improves cross modal localization robustness by disentangling visually-indicated audio representation. During training, we take advantage of the naturally occurring audio-visual temporal synchronization as the “free” self-supervision to avoid costly labeling. We also annotate and create the Epic Sounding Object dataset for evaluation purposes. Extensive experiments show that our method achieves state-of-the-art localization performance in egocentric videos and can be generalized to diverse audio-visual scenes.