比较分析：使用迁移学习从视频中识别暴力
Comparative Analysis: Violence Recognition from Videos using Transfer Learning
AI总结：
本研究在复杂数据集上对多种深度学习技术进行基准测试，发现数据集规模从500增至1600个视频后，四个模型的平均准确率提升了6%。
摘要：
动作识别已成为计算机视觉的热点话题。然而，计算机视觉在视频处理中的主要应用集中于检测相对简单的动作，而暴力检测等复杂事件的研究相对较少。本研究聚焦于在复杂数据集上对各种深度学习技术进行基准测试。接着，利用更大的数据集来测试数据量增加带来的提升。数据集规模从500个视频增加到1,600个视频，使得四个模型的平均准确率显著提高了6%。
英文摘要：
Action recognition has become a hot topic in computer vision. However, the main applications of computer vision in video processing have focused on detection of relatively simple actions while complex events such as violence detection have been comparatively less investigated. This study focuses on the benchmarking of various deep learning techniques on a complex dataset. Next, a larger dataset is utilized to test the uplift from increasing volume of data. The dataset size increase from 500 to 1,600 videos resulted in a notable average accuracy improvement of 6% across four models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
