Authors: Md Amirul Islam; Seyed Shahabeddin Nabavi; Irina Kezele; Yang Wang; Yuanhao Yu; Jin Tang
Description: In this paper, we tackle the problem of visually guided audio source separation in the context of both known and unknown objects (e.g., musical instruments). Recent successful end-to-end deep learning approaches adopt a single network with fixed parameters to generalize across unseen test videos. However, it can be challenging to generalize in cases where the distribution shift between training and test videos is higher as they fail to utilize internal information of unknown test videos. Based on this observation, we introduce a meta-consistency driven test time adaptation scheme that enables the pretrained model to quickly adapt to known and unknown test music videos in order to bring substantial improvements. In particular, we design a self-supervised audio-visual consistency objective as an auxiliary task that learns the synchronization between audio and its corresponding visual embedding. Concretely, we apply a meta-consistency training scheme to further optimize the pretrained model for effective and faster test time adaptation. We obtain substantial performance gains with only a smaller number of gradient updates and without any additional parameters for the task of audio source separation. Extensive experimental results across datasets demonstrate the effectiveness of our proposed method.
