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Video-language embeddings are a promising avenue for injecting semantics intovisual representations, but existing methods capture only short-termassociations between seconds-long video clips and their accompanying text. Wepropose HierVL, a novel hierarchical video-language embedding thatsimultaneously accounts for both long-term and short-term associations. Astraining data, we take videos accompanied by timestamped text descriptions ofhuman actions, together with a high-level text summary of the activitythroughout the long video (as are available in Ego4D). We introduce ahierarchical contrastive training objective that encourages text-visualalignment at both the clip level and video level. While the clip-levelconstraints use the step-by-step descriptions to capture what is happening inthat instant, the video-level constraints use the summary text to capture whyit is happening, i.e., the broader context for the activity and the intent ofthe actor. Our hierarchical scheme yields a clip representation thatoutperforms its single-level counterpart as well as a long-term videorepresentation that achieves SotA results on tasks requiring long-term videomodeling. HierVL successfully transfers to multiple challenging downstreamtasks (in EPIC-KITCHENS-100, Charades-Ego, HowTo100M) in both zero-shot andfine-tuned settings.

HierVL: Learning Hierarchical Video-Language Embeddings