论文标题
保留预训练的知识：以自我申斯的方式转移学习以进行行动识别
Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition
论文作者
论文摘要
基于视频的动作识别是计算机视觉中最受欢迎的主题之一。随着自我监视的视频表示方法的最新进展，行动识别通常是遵循两个阶段的训练框架，即对大规模的未标记集进行自我监督的预训练，并在下游标记的集合上进行转移学习。然而，灾难性的忘记预先训练的知识成为下游转移学习识别的主要问题，从而带来了次优的解决方案。在本文中，为了减轻上述问题，我们提出了一种新颖的转移学习方法，该方法结合了微调中的自我介绍，以保留从大规模数据集中学到的预训练模型中的知识。具体来说，我们将编码器从上一个时期作为教师模型进行了修复，以指导转移学习中当前时期对编码器的训练。通过如此简单而有效的学习策略，我们在行动识别任务中广泛使用UCF101和HMDB51数据集的最先进方法。
Video-based action recognition is one of the most popular topics in computer vision. With recent advances of selfsupervised video representation learning approaches, action recognition usually follows a two-stage training framework, i.e., self-supervised pre-training on large-scale unlabeled sets and transfer learning on a downstream labeled set. However, catastrophic forgetting of the pre-trained knowledge becomes the main issue in the downstream transfer learning of action recognition, resulting in a sub-optimal solution. In this paper, to alleviate the above issue, we propose a novel transfer learning approach that combines self-distillation in fine-tuning to preserve knowledge from the pre-trained model learned from the large-scale dataset. Specifically, we fix the encoder from the last epoch as the teacher model to guide the training of the encoder from the current epoch in the transfer learning. With such a simple yet effective learning strategy, we outperform state-of-the-art methods on widely used UCF101 and HMDB51 datasets in action recognition task.
