  In the face of the video data deluge, today's expensive clip-level
classifiers are increasingly impractical. We propose a framework for efficient
action recognition in untrimmed video that uses audio as a preview mechanism to
eliminate both short-term and long-term visual redundancies. First, we devise
an ImgAud2Vid framework that hallucinates clip-level features by distilling
from lighter modalities---a single frame and its accompanying audio---reducing
short-term temporal redundancy for efficient clip-level recognition. Second,
building on ImgAud2Vid, we further propose ImgAud-Skimming, an attention-based
long short-term memory network that iteratively selects useful moments in
untrimmed videos, reducing long-term temporal redundancy for efficient
video-level recognition. Extensive experiments on four action recognition
datasets demonstrate that our method achieves the state-of-the-art in terms of
both recognition accuracy and speed.
