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Modeling the visual changes that an action brings to a scene is critical forvideo understanding. Currently, CNNs process one local neighbourhood at a time,thus contextual relationships over longer ranges, while still learnable, areindirect. We present TROI, a plug-and-play module for CNNs to reason betweenmid-level feature representations that are otherwise separated in space andtime. The module relates localized visual entities such as hands andinteracting objects and transforms their corresponding regions of interestdirectly in the feature maps of convolutional layers. With TROI, we achievestate-of-the-art action recognition results on the large-scale datasetsSomething-Something-V2 and EPIC-Kitchens-100.

Transformed ROIs for Capturing Visual Transformations in Videos