Every Shot Counts: Using Exemplars for Repetition Counting in Videos
Saptarshi Sinha Alexandros Stergiou Dima Damen
Video repetition counting infers the number of repetitions of recurring
actions or motion within a video. We propose an exemplar-based approach that
discovers visual correspondence of video exemplars across repetitions within
target videos. Our proposed Every Shot Counts (ESCounts) model is an
attention-based encoder-decoder that encodes videos of varying lengths
alongside exemplars from the same and different videos. In training, ESCounts
regresses locations of high correspondence to the exemplars within the video.
In tandem, our method learns a latent that encodes representations of general
repetitive motions, which we use for exemplar-free, zero-shot inference.
Extensive experiments over commonly used datasets (RepCount, Countix, and
UCFRep) showcase ESCounts obtaining state-of-the-art performance across all
three datasets. On RepCount, ESCounts increases the off-by-one from 0.39 to
0.56 and decreases the mean absolute error from 0.38 to 0.21. Detailed
ablations further demonstrate the effectiveness of our method.