Temporal Transformer Networks With Self-Supervision for Action Recognition

In recent years, Internet of Things (IoT) has made rapid development, and IoT devices are developing toward intelligence. IoT terminal devices represented by surveillance cameras play an irreplaceable role in modern society, most of them are integrated with video action recognition and other intelligent functions. However, their performance is somewhat affected by the limitation of computing resources of IoT terminal devices and the lack of long-range nonlinear temporal relation modeling and reverse motion information modeling. To address this urgent problem, we introduce a startling temporal transformer network with self-supervision (TTSN). Our high-performance TTSN mainly consists of a temporal transformer module and a temporal sequence self-supervision (TSS) module. Concisely speaking, we utilize the efficient temporal transformer module to model the nonlinear temporal dependencies among nonlocal frames, which significantly enhances complex motion feature representations. The TSS module we employ unprecedentedly adopts the streamlined strategy of "random batch random channel" to reverse the sequence of video frames, allowing robust extractions of motion information representation from inversed temporal dimensions and improving the generalization capability of the model. Extensive experiments on three widely used data sets (HMDB51, UCF101, and Something-Something V1) have conclusively demonstrated that our proposed TTSN is promising as it successfully achieves state-of-the-art performance for video action recognition. Our TTSN provides the possibility for its application in IoT scenarios due to its computational complexity and high performance.