Retro-Actions: Learning ‘Close’ by Time-Reversing ‘Open’ Videos

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We investigate video transforms that result in classhomogeneous label-transforms. These are video transforms that consistently maintain or modify the labels of all videos in each class. We propose a general approach to discover invariant classes, whose transformed examples maintain their label; pairs of equivariant classes, whose transformed examples exchange their labels; and novelgenerating classes, whose transformed examples belong to a new class outside the dataset. Label transforms offer additional supervision previously unexplored in video recognition benefiting data augmentation and enabling zero-shot learning opportunities by learning a class from transformed
videos of its counterpart. Amongst such video transforms, we study horizontalflipping, time-reversal, and their composition. We highlight errors in naively using horizontal-flipping as a form of data augmentation in video. Next, we validate the realism of time-reversed videos through a human perception study where people exhibit equal preference for forward and time-reversed videos. Finally, we test our approach on two datasets, Jester and Something-Something, evaluating the three video transforms for zero-shot learning and data augmentation. Our results show that gestures such as ‘zooming in’ can be learnt from ‘zooming out’ in a zero-shot setting, as well as more complex actions with state transitions such as ‘digging something out of something’ from ‘burying something in something’.
Original languageEnglish
Title of host publicationIEEE/CVF International Conference on Computer Vision (ICCV) 2019
Number of pages10
Publication statusPublished - 2 Nov 2019
EventIEEE/CVF International Conference on Computer Vision (ICCV) 2019 - Korea, Seoul
Duration: 27 Oct 20192 Nov 2019


ConferenceIEEE/CVF International Conference on Computer Vision (ICCV) 2019


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