Is Space-Time Attention All You Need for Video Understanding? (2102.05095v4)

Abstract: We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named "TimeSformer," adapts the standard Transformer architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches. Our experimental study compares different self-attention schemes and suggests that "divided attention," where temporal attention and spatial attention are separately applied within each block, leads to the best video classification accuracy among the design choices considered. Despite the radically new design, TimeSformer achieves state-of-the-art results on several action recognition benchmarks, including the best reported accuracy on Kinetics-400 and Kinetics-600. Finally, compared to 3D convolutional networks, our model is faster to train, it can achieve dramatically higher test efficiency (at a small drop in accuracy), and it can also be applied to much longer video clips (over one minute long). Code and models are available at: https://github.com/facebookresearch/TimeSformer.

Review of TimeSformer: A Convolution-Free Approach to Video Understanding

The paper "Is Space-Time Attention All You Need for Video Understanding?" introduces a novel approach to video classification through the utilization of a model named "TimeSformer." This model leverages self-attention mechanisms across spatial and temporal dimensions, effectively removing the need for convolutions traditionally used in video understanding tasks.

Model Overview

TimeSformer is essentially an adaptation of the Vision Transformer (ViT) for video data. The method decomposes video frames into non-overlapping patches and employs a sequence of self-attention steps to learn spatiotemporal relationships. The primary distinguishing aspect of TimeSformer is its convolution-free architecture, which relies exclusively on self-attention mechanisms.

Architecture and Methodology

Decomposition into Patches: Each video frame is divided into patches, which are then linearly embedded into a feature space. Positional embeddings are added to retain information about the spatial and temporal location of each patch.

Self-Attention Mechanisms: The model explores several self-attention schemes:

• Space-Only Attention (S): Attention is computed within individual frames.
• Joint Space-Time Attention: Full spatiotemporal attention is computed across the entire video clip.
• Divided Space-Time Attention (T+S): This approach separates spatial and temporal attention, calculating them in sequence. This method emerged as the most effective in terms of both computational efficiency and accuracy.
• Sparse Local-Global Attention (L+G) and Axial Attention (T+W+H): Other proposed variations that offer different trade-offs between computational cost and performance.

Experimental Results

TimeSformer exhibits competitive performance across multiple benchmarks:

• Kinetics-400 and Kinetics-600: The model achieves state-of-the-art accuracy, highlighting its efficacy in video classification tasks that benefit from spatial and temporal modeling.
• Something-Something-V2 and Diving-48: These datasets, which require comprehensive temporal understanding, also showcase the promise of TimeSformer, albeit with marginal accuracy reductions compared to top performers.

Computational Efficiency: Divided Space-Time Attention results in significant computational savings, especially with increasing spatial resolution or video length. This characteristic makes TimeSformer more scalable compared to 3D convolution-based models like SlowFast and I3D, despite having a larger number of parameters.

Implications and Future Work

The implications of this research are substantial:

• Scalability: The enhanced efficiency of TimeSformer provides a framework that can handle longer video sequences and higher resolutions with lower computational costs, previously unattainable with 3D convolutional models.
• Learning Capacity: Reduced reliance on convolutional biases and the inherent flexibility of Transformer-based models enable TimeSformer to adapt to large-scale learning scenarios, potentially outperforming CNNs in data-rich environments.

Theoretical Implications: This research challenges the paradigm that convolutions are necessary for video understanding, suggesting that self-attention mechanisms alone can achieve or even surpass the performance of traditional convolutional networks.

Conclusion

The TimeSformer model proposed in this paper represents a pivotal shift in video understanding methodologies by demonstrating that comprehensive spatiotemporal attention can be effectively applied to video tasks. With its robust performance across a variety of datasets and tasks, coupled with lower computational demands, TimeSformer paves the way for future research into fully self-attention-based architectures and their applications in broader video analysis domains.

Potential future avenues of research include its application to different video analysis tasks like action localization, video captioning, and question-answering, further pushing the boundaries of what self-attention mechanisms can achieve in computer vision.