  The seen birds twitter, the running cars accompany with noise, etc. These
naturally audiovisual correspondences provide the possibilities to explore and
understand the outside world. However, the mixed multiple objects and sounds
make it intractable to perform efficient matching in the unconstrained
environment. To settle this problem, we propose to adequately excavate audio
and visual components and perform elaborate correspondence learning among them.
Concretely, a novel unsupervised audiovisual learning model is proposed, named
as \Deep Multimodal Clustering (DMC), that synchronously performs sets of
clustering with multimodal vectors of convolutional maps in different shared
spaces for capturing multiple audiovisual correspondences. And such integrated
multimodal clustering network can be effectively trained with max-margin loss
in the end-to-end fashion. Amounts of experiments in feature evaluation and
audiovisual tasks are performed. The results demonstrate that DMC can learn
effective unimodal representation, with which the classifier can even
outperform human performance. Further, DMC shows noticeable performance in
sound localization, multisource detection, and audiovisual understanding.
