Dark videos often lose essential information, which causes the knowledge
learned by networks is not enough to accurately recognize actions. Existing
knowledge assembling methods require massive GPU memory to distill the
knowledge from multiple teacher models into a student model. In action
recognition, this drawback becomes serious due to much computation required by
video process. Constrained by limited computation source, these approaches are
infeasible. To address this issue, we propose an unlimited knowledge
distillation (UKD) in this paper. Compared with existing knowledge assembling
methods, our UKD can effectively assemble different knowledge without
introducing high GPU memory consumption. Thus, the number of teaching models
for distillation is unlimited. With our UKD, the network's learned knowledge
can be remarkably enriched. Our experiments show that the single stream network
distilled with our UKD even surpasses a two-stream network. Extensive
experiments are conducted on the ARID dataset.

黑暗视频常常丢失关键信息，这导致网络所学到的知识不足以准确识别动作。现有的知识汇集方法需要大量 GPU 内存，将多个教师模型的知识提炼到一个学生模型中。在动作识别中，这个缺点由于视频处理需要大量计算而变得严重。受限于有限的计算资源，这些方法是不可行的。为了解决这个问题，在本文中我们提出了无限知识提炼（UKD）方法。与现有的知识汇集方法相比，我们的 UKD 可以有效地汇集不同的知识而不引入高 GPU 内存消耗。因此，进行提炼的教师模型数量是无限的。通过我们的 UKD，网络所学到的知识可以得到显著丰富。我们的实验证明，单流网络经过我们的 UKD 提炼甚至超过了双流网络。在 ARID 数据集上进行了大量实验。