  The objective of this paper is audio-visual synchronisation of general videos
'in the wild'. For such videos, the events that may be harnessed for
synchronisation cues may be spatially small and may occur only infrequently
during a many seconds-long video clip, i.e. the synchronisation signal is
'sparse in space and time'. This contrasts with the case of synchronising
videos of talking heads, where audio-visual correspondence is dense in both
time and space.
  We make four contributions: (i) in order to handle longer temporal sequences
required for sparse synchronisation signals, we design a multi-modal
transformer model that employs 'selectors' to distil the long audio and visual
streams into small sequences that are then used to predict the temporal offset
between streams. (ii) We identify artefacts that can arise from the compression
codecs used for audio and video and can be used by audio-visual models in
training to artificially solve the synchronisation task. (iii) We curate a
dataset with only sparse in time and space synchronisation signals; and (iv)
the effectiveness of the proposed model is shown on both dense and sparse
datasets quantitatively and qualitatively.
  Project page: v-iashin.github.io/SparseSync
