Gaze following estimates gaze targets of in-scene person by understanding human behavior and scene information. Existing methods usually analyze scene images for gaze following. However, compared with visual images, audio also provides crucial cues for determining human behavior.This suggests that we can further improve gaze following considering audio cues. In this paper, we explore gaze following tasks in conversational scenarios. We propose a novel multi-modal gaze following framework based on our observation ``audiences tend to focus on the speaker''. We first leverage the correlation between audio and lips, and classify speakers and listeners in a scene. We then use the identity information to enhance scene images and propose a gaze candidate estimation network. The network estimates gaze candidates from enhanced scene images and we use MLP to match subjects with candidates as classification tasks. Existing gaze following datasets focus on visual images while ignore audios.To evaluate our method, we collect a conversational dataset, VideoGazeSpeech (VGS), which is the first gaze following dataset including images and audio. Our method significantly outperforms existing methods in VGS datasets. The visualization result also prove the advantage of audio cues in gaze following tasks. Our work will inspire more researches in multi-modal gaze following estimation.

使用音频线索，本文在对话场景中提出了一种基于多模式的凝视追踪框架，利用音频与嘴唇之间的关联来增强场景图像并估计凝视候选者，采用多层感知机将主题与候选者进行匹配作为分类任务，通过引入图像和音频的对话数据集进行评估，表明我们的方法在凝视追踪任务中具有显著优势，并促进了多模式凝视追踪估计的更多研究。

会话情景中的多模态注视跟踪