  Instrument playing is among the most common scenes in music-related videos,
which represent nowadays one of the largest sources of online videos. In order
to understand the instrument-playing scenes in the videos, it is important to
know what instruments are played, when they are played, and where the playing
actions occur in the scene. While audio-based recognition of instruments has
been widely studied, the visual aspect of the music instrument playing remains
largely unaddressed in the literature. One of the main obstacles is the
difficulty in collecting annotated data of the action locations for
training-based methods. To address this issue, we propose a weakly-supervised
framework to find when and where the instruments are played in the videos. We
propose to use two auxiliary models, a sound model and an object model, to
provide supervisions for training the instrument-playing action model. The
sound model provides temporal supervisions, while the object model provides
spatial supervisions. They together can simultaneously provide temporal and
spatial supervisions. The resulted model only needs to analyze the visual part
of a music video to deduce which, when and where instruments are played. We
found that the proposed method significantly improves the localization
accuracy. We evaluate the result of the proposed method temporally and
spatially on a small dataset (totally 5,400 frames) that we manually annotated.
