This paper proposes a new strategy for learning powerful cross-modal
embeddings for audio-to-video synchronization. Here, we set up the problem as
one of cross-modal retrieval, where the objective is to find the most relevant
audio segment given a short video clip. The method builds on the recent
advances in learning representations from cross-modal self-supervision.
The main contributions of this paper are as follows: (1) we propose a new
learning strategy where the embeddings are learnt via a multi-way matching
problem, as opposed to a binary classification (matching or non-matching)
problem as proposed by recent papers; (2) we demonstrate that performance of
this method far exceeds the existing baselines on the synchronization task; (3)
we use the learnt embeddings for visual speech recognition in self-supervision,
and show that the performance matches the representations learnt end-to-end in
a fully-supervised manner.

该论文提出了一种新的跨模态嵌入学习策略，通过多路匹配问题学习嵌入，显著提升了音频到视频同步任务的表现，并用学习到的嵌入进行自我监督的视觉语音识别。