  A large number of annotated video-caption pairs are required for training
video captioning models, resulting in high annotation costs. Active learning
can be instrumental in reducing these annotation requirements. However, active
learning for video captioning is challenging because multiple semantically
similar captions are valid for a video, resulting in high entropy outputs even
for less-informative samples. Moreover, video captioning algorithms are
multimodal in nature with a visual encoder and language decoder. Further, the
sequential and combinatorial nature of the output makes the problem even more
challenging. In this paper, we introduce MAViC which leverages our proposed
Multimodal Semantics Aware Sequential Entropy (M-SASE) based acquisition
function to address the challenges of active learning approaches for video
captioning. Our approach integrates semantic similarity and uncertainty of both
visual and language dimensions in the acquisition function. Our detailed
experiments empirically demonstrate the efficacy of M-SASE for active learning
for video captioning and improve on the baselines by a large margin.
