2019/06/13 by Suhas Lohit, Qiao Wang, Lohit, Suhas +3 · 2 citations
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Time Series Analysis and Forecasting #cs.CV
paper · pdf · doi:10.48550/arxiv.1906.05947
Published in CVPR 2019, Codes available at https://github.com/suhaslohit/TTN
arxiv created 2019/06/13 · openalex publication_date 2019/06/13 · arxiv updated 2019/06/17 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Many time-series classification problems involve developing metrics that are invariant to temporal misalignment. In human activity analysis, temporal misalignment arises due to various reasons including differing initial phase, sensor sampling rates, and elastic time-warps due to subject-specific biomechanics. Past work in this area has only looked at reducing intra-class variability by elastic temporal alignment. In this paper, we propose a hybrid model-based and data-driven approach to learn warping functions that not just reduce intra-class variability, but also increase inter-class separation. We call this a temporal transformer network (TTN). TTN is an interpretable differentiable module, which can be easily integrated at the front end of a classification network. The module is capable of reducing intra-class variance by generating input-dependent warping functions which lead to rate-robust representations. At the same time, it increases inter-class variance by learning warping functions that are more discriminative. We show improvements over strong baselines in 3D action recognition on challenging datasets using the proposed framework. The improvements are especially pronounced when training sets are smaller.