AR-Net: Adaptive Frame Resolution for Efficient Action Recognition (2007.15796v1)

Abstract: Action recognition is an open and challenging problem in computer vision. While current state-of-the-art models offer excellent recognition results, their computational expense limits their impact for many real-world applications. In this paper, we propose a novel approach, called AR-Net (Adaptive Resolution Network), that selects on-the-fly the optimal resolution for each frame conditioned on the input for efficient action recognition in long untrimmed videos. Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency. We efficiently train the policy network jointly with the recognition model using standard back-propagation. Extensive experiments on several challenging action recognition benchmark datasets well demonstrate the efficacy of our proposed approach over state-of-the-art methods. The project page can be found at https://mengyuest.github.io/AR-Net

• The paper presents AR-Net, which adaptively selects frame resolutions via a policy network using Gumbel Softmax, enhancing action recognition efficiency.
• The proposed framework is model-agnostic, integrating with various backbones like ResNet and EfficientNet to balance computation and accuracy dynamically.
• Experimental results on datasets like ActivityNet-v1.3 show up to a 5% accuracy improvement while significantly lowering FLOPS for real-time applications.

Overview of AR-Net: Adaptive Frame Resolution for Efficient Action Recognition

The paper "AR-Net: Adaptive Frame Resolution for Efficient Action Recognition" presents a method to enhance the efficiency of action recognition in videos by adaptively selecting the optimal resolution for each frame. This novel approach addresses the challenge of computational expense in state-of-the-art action recognition models, which often hinders their applicability in resource-constrained settings.

Methodology

The core contribution of this research is the AR-Net framework, which integrates a policy network with action recognition models to dynamically choose resolutions for video frames. The framework aims to balance accuracy and computation efficiency by utilizing different resolutions based on the relevance of each frame.

1. Adaptive Resolution Selection: AR-Net utilizes a policy network that determines the best resolution for each frame. The policy is learned using Gumbel Softmax sampling, enabling the system to train efficiently through backpropagation rather than more complex reinforcement learning approaches.
2. Model Architecture: The framework is model-agnostic and can be applied to various backbone architectures like ResNet and EfficientNet. The decision for frame resolution is carried out on-the-fly, leveraging lightweight networks to process frames at lower resolutions, only resorting to higher resolutions when necessary.
3. Efficiency Gains: The method employs a mix of resolutions and frame skipping strategies to significantly reduce the number of Floating Point Operations (FLOPS) required. This is particularly advantageous for applications like real-time analysis on mobile devices or within autonomous systems, where computational resources are limited.

Experimental Results

The paper's authors conduct extensive experiments on large-scale datasets, including ActivityNet-v1.3, FCVID, and Mini-Kinetics, showcasing AR-Net's superior performance compared to existing methods.

• Efficiency and Performance: AR-Net achieves comparable or improved action recognition performance while dramatically reducing computational demands. For instance, on the ActivityNet-v1.3 dataset, AR-Net improves recognition accuracy by up to 5% over baseline models while reducing FLOPS by significant margins.
• Framework Generalization: The adaptive resolution approach proves to be highly versatile, maintaining efficacy across different backbone architectures and datasets, underscoring its potential for broad application.

Implications and Future Directions

The implications of AR-Net are significant for both practical deployments and theoretical advancements in video analysis:

• Real-World Applications: The reduced computational load opens avenues for deploying advanced action recognition models in constrained environments like embedded systems or mobile devices.
• Theoretical Contributions: This work provides insights into the benefits of dynamic computation and adaptive learning mechanisms within neural networks, which could inspire further research in efficiency-centric AI techniques.
• Future Enhancements: Ongoing developments could explore integrating AR-Net with multi-modal data (e.g., audio-visual) to further improve performance. Additionally, extending adaptive strategies to other aspects of model computation beyond resolution could yield even more resource-efficient architectures.

In summary, AR-Net offers a pragmatic advancement in action recognition technology, striking a balance between computational efficiency and model accuracy, with wide-ranging impacts on both applied and theoretical aspects of AI research. The method sets a benchmark for future work targeting the optimization of deep learning models for real-time, resource-intensive applications.