Human action recognition in dark videos is a challenging task for computer
vision. Recent research focuses on applying dark enhancement methods to improve
the visibility of the video. However, such video processing results in the loss
of critical information in the original (un-enhanced) video. Conversely,
traditional two-stream methods are capable of learning information from both
original and processed videos, but it can lead to a significant increase in the
computational cost during the inference phase in the task of video
classification. To address these challenges, we propose a novel teacher-student
video classification framework, named Dual-Light KnowleDge Distillation for
Action Recognition in the Dark (DL-KDD). This framework enables the model to
learn from both original and enhanced video without introducing additional
computational cost during inference. Specifically, DL-KDD utilizes the strategy
of knowledge distillation during training. The teacher model is trained with
enhanced video, and the student model is trained with both the original video
and the soft target generated by the teacher model. This teacher-student
framework allows the student model to predict action using only the original
input video during inference. In our experiments, the proposed DL-KDD framework
outperforms state-of-the-art methods on the ARID, ARID V1.5, and Dark-48
datasets. We achieve the best performance on each dataset and up to a 4.18%
improvement on Dark-48, using only original video inputs, thus avoiding the use
of two-stream framework or enhancement modules for inference. We further
validate the effectiveness of the distillation strategy in ablative
experiments. The results highlight the advantages of our knowledge distillation
framework in dark human action recognition.

我们提出了一种新颖的师生视频分类框架 DL-KDD，它能够在不引入额外计算成本的情况下，从原始和增强视频中学习，通过知识蒸馏策略训练师傅模型和学生模型，使学生模型在推理过程中仅使用原始输入视频来预测动作，实验证实了这种蒸馏策略在暗光环境下人体动作识别中的有效性。

DL-KDD: 暗光条件下行动识别的双光知识蒸馏

DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the  Dark

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。

黑暗环境中的行动识别的无限知识蒸馏

Unlimited Knowledge Distillation for Action Recognition in the Dark

The task of action recognition in dark videos is useful in various scenarios,
e.g., night surveillance and self-driving at night. Though progress has been
made in the action recognition task for videos in normal illumination, few have
studied action recognition in the dark. This is partly due to the lack of
sufficient datasets for such a task. In this paper, we explored the task of
action recognition in dark videos. We bridge the gap of the lack of data for
this task by collecting a new dataset: the Action Recognition in the Dark
(ARID) dataset. It consists of over 3,780 video clips with 11 action
categories. To the best of our knowledge, it is the first dataset focused on
human actions in dark videos. To gain further understandings of our ARID
dataset, we analyze the ARID dataset in detail and exhibited its necessity over
synthetic dark videos. Additionally, we benchmarked the performance of several
current action recognition models on our dataset and explored potential methods
for increasing their performances. Our results show that current action
recognition models and frame enhancement methods may not be effective solutions
for the task of action recognition in dark videos.

本文介绍了新采集的 Action Recognition in the Dark（ARID）数据集，用于行为识别。研究分析了该数据集，并评估了当前行为识别模型及框架增强方法对于暗视频识别的效果。结果表明，现有的行为识别模型和框架增强方法不适用于暗视频的行为识别任务。