Convolutional video models have an order of magnitude larger computational complexity than their counterpart image-level models. Constrained by computational resources, there is no model or training method that can train long video sequences end-to-end. Currently, the main-stream method is to split a raw video into clips, leading to incomplete fragmentary temporal information flow. Inspired by natural language processing techniques dealing with long sentences, we propose to treat videos as serial fragments satisfying Markov property, and train it as a whole by progressively propagating information through the temporal dimension in multiple steps. This progressive training (PGT) method is able to train long videos end-to-end with limited resources and ensures the effective transmission of information. As a general and robust training method, we empirically demonstrate that it yields significant performance improvements on different models and datasets. As an illustrative example, the proposed method improves SlowOnly network by 3.7 mAP on Charades and 1.9 top-1 accuracy on Kinetics with negligible parameter and computation overhead. Code is available at https://github.com/BoPang1996/PGT.

我们提出了逐步传递信息的逐步训练（PGT）方法，将视频视为满足马尔可夫属性的串行片段，并在多个步骤中沿时间维度逐渐传播信息来整体训练它，从而在不需要太多资源的情况下能够端到端地训练长时间的视频，并确保了信息的有效传输，其经验结果表明，它在不同的模型和数据集上得到了显着的性能提高。

PGT: 在长视频上训练模型的渐进式方法