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We present a method to learn a representation for adverbs from instructionalvideos using weak supervision from the accompanying narrations. Key to ourmethod is the fact that the visual representation of the adverb is highlydependant on the action to which it applies, although the same adverb willmodify multiple actions in a similar way. For instance, while 'spread quickly'and 'mix quickly' will look dissimilar, we can learn a common representationthat allows us to recognize both, among other actions. We formulate this as anembedding problem, and use scaled dot-product attention to learn fromweakly-supervised video narrations. We jointly learn adverbs as invertibletransformations operating on the embedding space, so as to add or remove theeffect of the adverb. As there is no prior work on weakly supervised learningfrom adverbs, we gather paired action-adverb annotations from a subset of theHowTo100M dataset for 6 adverbs: quickly/slowly, finely/coarsely, andpartially/completely. Our method outperforms all baselines for video-to-adverbretrieval with a performance of 0.719 mAP. We also demonstrate our model'sability to attend to the relevant video parts in order to determine the adverbfor a given action.

Action Modifiers: Learning from Adverbs in Instructional Videos