阅读一篇论文的摘要并分点总结给我中英文总结In the field of video self-supervised learning contrastive instance learning methods suffer from a lack of semanticinformation resulting in inadequate generalization in downstre

中文总结：

本文研究视频自监督学习领域中，对比实例学习方法缺乏语义信息，导致下游任务泛化不足的问题。为了解决这个问题，本文提出了一个基于快慢路径的视频自监督跨路径训练模型（VCSF）。该模型从纯RGB视频帧中分别提取时间和空间特征，并使用两个路径的互补表示进行跨路径训练。此外，本文提出了一个运动感知模块，以增强网络感知快速变化的人体动作能力。在UCF101和HMDB51的下游任务中进行了大量实验，并在使用UCF101数据集进行自监督预训练的模型中获得了最先进的结果，包括运动识别和最近邻检索。

英文总结：

• Contrastive instance learning methods suffer from a lack of semantic information in video self-supervised learning, resulting in inadequate generalization in downstream tasks.
• Optical flow can provide some semantic information, but it requires significant computational cost prior to training.
• The proposed VCSF model separately extracts temporal and spatial features from pure RGB video frames and uses the complementary representations of the two pathways to conduct cross-pathway training.
• The motion perception module in the low-frame-rate space enhances the network's ability to perceive rapidly changing human motion.
• Extensive experiments were conducted in downstream tasks of UCF101 and HMDB51, and state-of-the-art results were obtained in models using the UCF101 dataset for self-supervised pre-training, including motion recognition and nearest neighbor retrieval