Progressive Knowledge Distillation From Different Levels of Teachers for Online Action Detection

In this paper, we explore the problem of Online Action Detection (OAD), where the task is to detect ongoing actions from streaming videos without access to video frames in the future. Existing methods achieve good detection performance by capturing long-range temporal structures. However, a major challenge of this task is to detect actions at a specific time that arrive with insufficient observations. In this work, we utilize the additional future frames available at the training phase and propose a novel Knowledge Distillation (KD) framework for OAD, where a teacher network looks at more frames from the future and the student network distills the knowledge from the teacher for detecting ongoing actions from the observation up to the current frames. Usually, the conventional KD regards a high-level teacher network (i.e., the network after the last training iteration) to guide the student network throughout all training iterations, which may result in poor distillation due to the large knowledge gap between the high-level teacher and the student network at early training iterations. To remedy this, we propose a novel progressive knowledge distillation from different levels of teachers (PKD-DLT) for OAD, where in addition to a high-level teacher, we also generate several low- and middle-level teachers, and progressively transfer the knowledge (in the order of low- to high-level) to the student network throughout training iterations, for effective distillation. Evaluated on two challenging datasets THUMOS14 and TVSeries, we validate that our PKD-DLT is an effective teacher-student learning paradigm, which can be a plug-in to improve the performance of the existing OAD models and achieve a state-of-the-art.