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This technical report describes the CONE approach for Ego4D Natural LanguageQueries (NLQ) Challenge in ECCV 2022. We leverage our model CONE, an efficientwindow-centric COarse-to-fiNE alignment framework. Specifically, CONEdynamically slices the long video into candidate windows via a sliding windowapproach. Centering at windows, CONE (1) learns the inter-window(coarse-grained) semantic variance through contrastive learning and speeds upinference by pre-filtering the candidate windows relevant to the NL query, and(2) conducts intra-window (fine-grained) candidate moments ranking utilizingthe powerful multi-modal alignment ability of the contrastive vision-textpre-trained model EgoVLP. On the blind test set, CONE achieves 15.26 and 9.24for R1@IoU=0.3 and R1@IoU=0.5, respectively.

An Efficient COarse-to-fiNE Alignment Framework @ Ego4D Natural Language Queries Challenge 2022