We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm
for online action detection, which employs a long- and short-term memory
mechanism to model prolonged sequence data. It consists of an LSTR encoder that
dynamically leverages coarse-scale historical information from an extended
temporal window (e.g., 2048 frames spanning of up to 8 minutes), together with
an LSTR decoder that focuses on a short time window (e.g., 32 frames spanning 8
seconds) to model the fine-scale characteristics of the data. Compared to prior
work, LSTR provides an effective and efficient method to model long videos with
fewer heuristics, which is validated by extensive empirical analysis. LSTR
achieves state-of-the-art performance on three standard online action detection
benchmarks, THUMOS'14, TVSeries, and HACS Segment. Code has been made available
at: this https URL

提出了一种长短时 Transformer（LSTR）算法，用于在线动作检测，它采用了长短时记忆机制来建模长序列数据，其中编码器动态地利用拓展的时间窗口的历史信息，解码器专注于一个短时间窗口来建模数据的细节特征。与先前的工作相比，LSTR 提供了一种有效且高效的建模长视频的方法，其在三种标准的在线动作检测基准测试（THUMOS'14、TVSeries 和 HACS Segment）上实现了最先进的性能。