  In recent years, the thriving development of research related to egocentric
videos has provided a unique perspective for the study of conversational
interactions, where both visual and audio signals play a crucial role. While
most prior work focus on learning about behaviors that directly involve the
camera wearer, we introduce the Ego-Exocentric Conversational Graph Prediction
problem, marking the first attempt to infer exocentric conversational
interactions from egocentric videos. We propose a unified multi-modal framework
-- Audio-Visual Conversational Attention (AV-CONV), for the joint prediction of
conversation behaviors -- speaking and listening -- for both the camera wearer
as well as all other social partners present in the egocentric video.
Specifically, we adopt the self-attention mechanism to model the
representations across-time, across-subjects, and across-modalities. To
validate our method, we conduct experiments on a challenging egocentric video
dataset that includes multi-speaker and multi-conversation scenarios. Our
results demonstrate the superior performance of our method compared to a series
of baselines. We also present detailed ablation studies to assess the
contribution of each component in our model. Check our project page at
https://vjwq.github.io/AV-CONV/.
