F4D: Factorized 4D Convolutional Neural Network for Efficient Video-level Representation Learning
This addresses the need for efficient video-level context modeling in action recognition, though it appears incremental as it builds on existing CNN methods.
The paper tackles the problem of video-level representation learning for action recognition by proposing a factorized 4D CNN architecture (F4D) that captures long-range spatiotemporal structures, resulting in significant performance improvements over conventional 2D and 3D CNNs on five benchmark datasets.
Recent studies have shown that video-level representation learning is crucial to the capture and understanding of the long-range temporal structure for video action recognition. Most existing 3D convolutional neural network (CNN)-based methods for video-level representation learning are clip-based and focus only on short-term motion and appearances. These CNN-based methods lack the capacity to incorporate and model the long-range spatiotemporal representation of the underlying video and ignore the long-range video-level context during training. In this study, we propose a factorized 4D CNN architecture with attention (F4D) that is capable of learning more effective, finer-grained, long-term spatiotemporal video representations. We demonstrate that the proposed F4D architecture yields significant performance improvements over the conventional 2D, and 3D CNN architectures proposed in the literature. Experiment evaluation on five action recognition benchmark datasets, i.e., Something-Something-v1, SomethingSomething-v2, Kinetics-400, UCF101, and HMDB51 demonstrate the effectiveness of the proposed F4D network architecture for video-level action recognition.