  Zero-shot Natural Language-Video Localization (NLVL) methods have exhibited
promising results in training NLVL models exclusively with raw video data by
dynamically generating video segments and pseudo-query annotations. However,
existing pseudo-queries often lack grounding in the source video, resulting in
unstructured and disjointed content. In this paper, we investigate the
effectiveness of commonsense reasoning in zero-shot NLVL. Specifically, we
present CORONET, a zero-shot NLVL framework that leverages commonsense to
bridge the gap between videos and generated pseudo-queries via a commonsense
enhancement module. CORONET employs Graph Convolution Networks (GCN) to encode
commonsense information extracted from a knowledge graph, conditioned on the
video, and cross-attention mechanisms to enhance the encoded video and
pseudo-query representations prior to localization. Through empirical
evaluations on two benchmark datasets, we demonstrate that CORONET surpasses
both zero-shot and weakly supervised baselines, achieving improvements up to
32.13% across various recall thresholds and up to 6.33% in mIoU. These results
underscore the significance of leveraging commonsense reasoning for zero-shot
NLVL.
