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Convolutional video models have an order of magnitude larger computationalcomplexity than their counterpart image-level models. Constrained bycomputational resources, there is no model or training method that can trainlong video sequences end-to-end. Currently, the main-stream method is to splita raw video into clips, leading to incomplete fragmentary temporal informationflow. Inspired by natural language processing techniques dealing with longsentences, we propose to treat videos as serial fragments satisfying Markovproperty, and train it as a whole by progressively propagating informationthrough the temporal dimension in multiple steps. This progressive training(PGT) method is able to train long videos end-to-end with limited resources andensures the effective transmission of information. As a general and robusttraining method, we empirically demonstrate that it yields significantperformance improvements on different models and datasets. As an illustrativeexample, the proposed method improves SlowOnly network by 3.7 mAP on Charadesand 1.9 top-1 accuracy on Kinetics with negligible parameter and computationoverhead. Code is available at https://github.com/BoPang1996/PGT.

PGT: A Progressive Method for Training Models on Long Videos