Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition (2307.06947v4)

Abstract: Recent video recognition models utilize Transformer models for long-range spatio-temporal context modeling. Video transformer designs are based on self-attention that can model global context at a high computational cost. In comparison, convolutional designs for videos offer an efficient alternative but lack long-range dependency modeling. Towards achieving the best of both designs, this work proposes Video-FocalNet, an effective and efficient architecture for video recognition that models both local and global contexts. Video-FocalNet is based on a spatio-temporal focal modulation architecture that reverses the interaction and aggregation steps of self-attention for better efficiency. Further, the aggregation step and the interaction step are both implemented using efficient convolution and element-wise multiplication operations that are computationally less expensive than their self-attention counterparts on video representations. We extensively explore the design space of focal modulation-based spatio-temporal context modeling and demonstrate our parallel spatial and temporal encoding design to be the optimal choice. Video-FocalNets perform favorably well against the state-of-the-art transformer-based models for video recognition on five large-scale datasets (Kinetics-400, Kinetics-600, SS-v2, Diving-48, and ActivityNet-1.3) at a lower computational cost. Our code/models are released at https://github.com/TalalWasim/Video-FocalNets.

Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition

The paper introduces Video-FocalNet, an innovative architecture for video action recognition that aims to offer an efficient balance between the capabilities of convolutional neural networks (CNNs) and Vision Transformers (ViTs). Recognizing the need for effective spatio-temporal modeling in video recognition, this architecture leverages a spatio-temporal focal modulation strategy as a means to efficiently model both local and global contexts.

Traditional video recognition approaches typically face a trade-off between accuracy and computational requirements. CNNs, known for their efficiency in modeling local patterns due to their localized connectivity and translational equivariance, fall short when it comes to capturing long-range dependencies essential for comprehensive video context understanding. Conversely, ViTs have demonstrated the capability to model such long-range dependencies effectively, thanks to the self-attention mechanism adapted from natural language processing. However, this comes at the expense of substantial computational resources, limiting their utility in resource-constrained environments.

Focal Modulation Technique

The focal modulation strategy central to Video-FocalNet reorders the standard operation sequence of self-attention mechanisms to reduce computational demand. Instead of beginning with a computationally expensive query-key interaction followed by aggregation, the focal modulation initially aggregates the local context before interacting it with the query token. This approach benefits from using depthwise and pointwise convolutions, which are less computationally intensive than the standard matrix multiplications used in the self-attention operation.

Design and Architecture

Video-FocalNet exploits a parallel processing design for separate spatial and temporal information streams, allowing effective incorporation of both intra-frame (spatial) and inter-frame (temporal) contexts. Through hierarchical aggregation, spatial and temporal modulators are generated and subsequently integrated with the query tokens. This design is proposed to improve both efficiencу and accuracy, surpassing the computational cost limitations inherent in self-attention-based ViTs. The team presents various configurations of Video-FocalNet ranging from tiny to base models, showcasing scalability and flexibility to cater to different performance requirements.

Experimental Validation

The researchers conducted comprehensive experiments across five large-scale datasets, including Kinetics-400, Kinetics-600, and Something-Something-v2, comparative benchmarks frequently employed for video action recognition research. The empirical results underscore the competitiveness of Video-FocalNet, which delivers state-of-the-art accuracy with reduced computational complexities. Specifically, Video-FocalNet-B achieved superior performance on Kinetics-600 with an accuracy improvement of 1.2% compared to the previous best methodologies, highlighting its effectiveness in long-range spatio-temporal dependency modeling.

Implications and Future Work

Video-FocalNet's contribution lies primarily in its ability to harness the strengths of convolutional operations to reduce the dependency on resource-intensive self-attention, potentially opening new avenues for efficient video processing solutions. The unique focal modulation technique could be extrapolated to other domains where spatio-temporal dependencies are crucial, suggesting possibilities for novel applications such as real-time video processing and streaming analytics.

Future research directions could delve into further optimizing the focal modulation architecture, perhaps by combining it with other efficient neural network architectures or exploring hybrid models that further reduce real-time computational burdens. Moreover, expanding the applicability of Video-FocalNet to tasks beyond standard video classification, such as dense prediction tasks, could also be an area of fruitful exploration.

In summary, Video-FocalNets stand as a promising approach for efficient video action recognition, combining effective spatio-temporal context modeling with reduced computational overhead, poised to influence future architectures in video and spatio-temporal processing realms.