Diverse actions give rise to rich audio-visual signals in
long videos. Recent works showcase that the two modalities of audio and video exhibit different temporal extents of
events and distinct labels. We address the interplay between
the two modalities in long videos by explicitly modelling the
temporal extents of audio and visual events. We propose
the Time Interval Machine (TIM) where a modality-specific
time interval poses as a query to a transformer encoder that
ingests a long video input. The encoder then attends to the
specified interval, as well as the surrounding context in both
modalities, in order to recognise the ongoing action.
We test TIM on three long audio-visual video datasets:
EPIC-KITCHENS, Perception Test, and AVE, reporting state-of-the-art (SOTA) for recognition. On EPICKITCHENS, we beat previous SOTA that utilises LLMs and
significantly larger pre-training by 2.9% top-1 action recognition accuracy. Additionally, we show that TIM can be
adapted for action detection, using dense multi-scale interval queries, outperforming SOTA on EPIC-KITCHENS-100
for most metrics, and showing strong performance on the
Perception Test. Our ablations show the critical role of integrating the two modalities and modelling their time intervals in achieving this performance. Code and models at:
https://github.com/JacobChalk/TIM.
