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Recent action recognition models have achieved impressive results byintegrating objects, their locations and interactions. However, obtaining densestructured annotations for each frame is tedious and time-consuming, makingthese methods expensive to train and less scalable. At the same time, if asmall set of annotated images is available, either within or outside the domainof interest, how could we leverage these for a video downstream task? Wepropose a learning framework StructureViT (SViT for short), which demonstrateshow utilizing the structure of a small number of images only available duringtraining can improve a video model. SViT relies on two key insights. First, asboth images and videos contain structured information, we enrich a transformermodel with a set of \emph{object tokens} that can be used across images andvideos. Second, the scene representations of individual frames in video should"align" with those of still images. This is achieved via a \emph{Frame-ClipConsistency} loss, which ensures the flow of structured information betweenimages and videos. We explore a particular instantiation of scene structure,namely a \emph{Hand-Object Graph}, consisting of hands and objects with theirlocations as nodes, and physical relations of contact/no-contact as edges. SViTshows strong performance improvements on multiple video understanding tasks anddatasets. Furthermore, it won in the Ego4D CVPR'22 Object State Localizationchallenge. For code and pretrained models, visit the project page at\url{https://eladb3.github.io/SViT/}

Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens