ACTION-Net: Multipath Excitation for Action Recognition
This work addresses the efficiency-performance trade-off in action recognition for video analysis applications, presenting an incremental improvement by integrating novel excitation paths into existing 2D CNN architectures.
The paper tackles the challenge of balancing computational efficiency and performance in video action recognition by proposing ACTION-Net, a module that enhances 2D CNNs with spatio-temporal, channel, and motion excitation paths, achieving consistent improvements over 2D CNN baselines on datasets like Something-Something V2, Jester, and EgoGesture.
Spatial-temporal, channel-wise, and motion patterns are three complementary and crucial types of information for video action recognition. Conventional 2D CNNs are computationally cheap but cannot catch temporal relationships; 3D CNNs can achieve good performance but are computationally intensive. In this work, we tackle this dilemma by designing a generic and effective module that can be embedded into 2D CNNs. To this end, we propose a spAtio-temporal, Channel and moTion excitatION (ACTION) module consisting of three paths: Spatio-Temporal Excitation (STE) path, Channel Excitation (CE) path, and Motion Excitation (ME) path. The STE path employs one channel 3D convolution to characterize spatio-temporal representation. The CE path adaptively recalibrates channel-wise feature responses by explicitly modeling interdependencies between channels in terms of the temporal aspect. The ME path calculates feature-level temporal differences, which is then utilized to excite motion-sensitive channels. We equip 2D CNNs with the proposed ACTION module to form a simple yet effective ACTION-Net with very limited extra computational cost. ACTION-Net is demonstrated by consistently outperforming 2D CNN counterparts on three backbones (i.e., ResNet-50, MobileNet V2 and BNInception) employing three datasets (i.e., Something-Something V2, Jester, and EgoGesture). Codes are available at \url{https://github.com/V-Sense/ACTION-Net}.