Multi-Resolution Audio-Visual Feature Fusion for Temporal Action
  Localization
Edward Fish Jon Weinbren Andrew Gilbert
Temporal Action Localization (TAL) aims to identify actions' start, end, and
class labels in untrimmed videos. While recent advancements using transformer
networks and Feature Pyramid Networks (FPN) have enhanced visual feature
recognition in TAL tasks, less progress has been made in the integration of
audio features into such frameworks. This paper introduces the Multi-Resolution
Audio-Visual Feature Fusion (MRAV-FF), an innovative method to merge
audio-visual data across different temporal resolutions. Central to our
approach is a hierarchical gated cross-attention mechanism, which discerningly
weighs the importance of audio information at diverse temporal scales. Such a
technique not only refines the precision of regression boundaries but also
bolsters classification confidence. Importantly, MRAV-FF is versatile, making
it compatible with existing FPN TAL architectures and offering a significant
enhancement in performance when audio data is available.