Communicating in noisy, multi-talker environments is challenging, especially
for people with hearing impairments. Egocentric video data can potentially be
used to identify a user's conversation partners, which could be used to inform
selective acoustic amplification of relevant speakers. Recent introduction of
datasets and tasks in computer vision enable progress towards analyzing social
interactions from an egocentric perspective. Building on this, we focus on the
task of identifying conversation partners from egocentric video and describe a
suitable dataset. Our dataset comprises 69 hours of egocentric video of diverse
multi-conversation scenarios where each individual was assigned one or more
conversation partners, providing the labels for our computer vision task. This
dataset enables the development and assessment of algorithms for identifying
conversation partners and evaluating related approaches. Here, we describe the
dataset alongside initial baseline results of this ongoing work, aiming to
contribute to the exciting advancements in egocentric video analysis for social
settings.

基于现有的计算机视觉数据集和任务，本研究侧重于从自我的视角识别会话伙伴，并描述了一个合适的数据集，该数据集包含了 69 小时的多样化多对话场景的自我的视频，对每个个体分配了一个或多个对话伙伴，为我们的计算机视觉任务提供了标签。通过这个数据集，我们可以开发和评估用于识别会话伙伴和相关方法的算法。该研究为自我的视频分析在社交环境中的令人兴奋的进展做出贡献。