Activity Progress Prediction

Abstract

In this paper, we investigate the behaviour of current progress prediction methods on the currently used benchmark datasets. We show that the progress prediction methods can fail to extract useful information from visual data on these datasets. Moreover, when the methods fail to extract visual information, memory-based methods adopt a frame-counting strategy when presented with extsl{full-video} data as input. Additionally, we evaluate all the methods on a synthetic dataset we specifically designed for the progress prediction task. On our synthetic dataset the results show that all the methods can make use of the visual information and outperform the native, non-learning baselines. We conclude that in its current form the task of progress prediction is ill-posed. The learning methods tend to fail to extract useful information from the visual data and instead rely purely on frame counting.