  There are rich synchronized audio and visual events in our daily life. Inside
the events, audio scenes are associated with the corresponding visual objects;
meanwhile, sounding objects can indicate and help to separate their individual
sounds in the audio track. Based on this observation, in this paper, we propose
a cyclic co-learning (CCoL) paradigm that can jointly learn sounding object
visual grounding and audio-visual sound separation in a unified framework.
Concretely, we can leverage grounded object-sound relations to improve the
results of sound separation. Meanwhile, benefiting from discriminative
information from separated sounds, we improve training example sampling for
sounding object grounding, which builds a co-learning cycle for the two tasks
and makes them mutually beneficial. Extensive experiments show that the
proposed framework outperforms the compared recent approaches on both tasks,
and they can benefit from each other with our cyclic co-learning.
