  Efficiency is an important issue in designing video architectures for action
recognition. 3D CNNs have witnessed remarkable progress in action recognition
from videos. However, compared with their 2D counterparts, 3D convolutions
often introduce a large amount of parameters and cause high computational cost.
To relieve this problem, we propose an efficient temporal module, termed as
Temporal Enhancement-and-Interaction (TEI Module), which could be plugged into
the existing 2D CNNs (denoted by TEINet). The TEI module presents a different
paradigm to learn temporal features by decoupling the modeling of channel
correlation and temporal interaction. First, it contains a Motion Enhanced
Module (MEM) which is to enhance the motion-related features while suppress
irrelevant information (e.g., background). Then, it introduces a Temporal
Interaction Module (TIM) which supplements the temporal contextual information
in a channel-wise manner. This two-stage modeling scheme is not only able to
capture temporal structure flexibly and effectively, but also efficient for
model inference. We conduct extensive experiments to verify the effectiveness
of TEINet on several benchmarks (e.g., Something-Something V1&V2, Kinetics,
UCF101 and HMDB51). Our proposed TEINet can achieve a good recognition accuracy
on these datasets but still preserve a high efficiency.
