2017/07/21 by Tianwei Lin, Xu Zhao, Lin, Tianwei +3 · 1 citation
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Multimodal Machine Learning Applications #cs.CV
paper · pdf · doi:10.48550/arxiv.1707.06750
4 pages, Presented at ActivityNet Large Scale Activity Recognition Challenge workshop at CVPR 2017
openalex publication_date 2017/07/21 · openalex created_date 2017/07/31 · arxiv created 2018/09/26 · arxiv updated 2018/09/27 · openalex updated_date 2026/07/28
In this notebook paper, we describe our approach in the submission to the temporal action proposal (task 3) and temporal action localization (task 4) of ActivityNet Challenge hosted at CVPR 2017. Since the accuracy in action classification task is already very high (nearly 90% in ActivityNet dataset), we believe that the main bottleneck for temporal action localization is the quality of action proposals. Therefore, we mainly focus on the temporal action proposal task and propose a new proposal model based on temporal convolutional network. Our approach achieves the state-of-the-art performances on both temporal action proposal task and temporal action localization task.