Perfect Match: Improved Cross-modal Embeddings for Audio-visual Synchronisation
| Author | Soo-Whan Chung, Joon Son Chung, Hong-Goo Kang |
| Publication | International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Month | May |
| Year | 2019 |
| Link | [Paper] [Github] |
ABSTRACT
This paper proposes a new strategy for learning powerful cross-modal embeddings for audio-to-video synchronisation. 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 synchronisation 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.