$A^2$-Nets: Double Attention Networks (1810.11579v1)

Abstract: Learning to capture long-range relations is fundamental to image/video recognition. Existing CNN models generally rely on increasing depth to model such relations which is highly inefficient. In this work, we propose the "double attention block", a novel component that aggregates and propagates informative global features from the entire spatio-temporal space of input images/videos, enabling subsequent convolution layers to access features from the entire space efficiently. The component is designed with a double attention mechanism in two steps, where the first step gathers features from the entire space into a compact set through second-order attention pooling and the second step adaptively selects and distributes features to each location via another attention. The proposed double attention block is easy to adopt and can be plugged into existing deep neural networks conveniently. We conduct extensive ablation studies and experiments on both image and video recognition tasks for evaluating its performance. On the image recognition task, a ResNet-50 equipped with our double attention blocks outperforms a much larger ResNet-152 architecture on ImageNet-1k dataset with over 40% less the number of parameters and less FLOPs. On the action recognition task, our proposed model achieves the state-of-the-art results on the Kinetics and UCF-101 datasets with significantly higher efficiency than recent works.

Overview of $A^2$-Nets: Double Attention Networks

The paper "$A^2$-Nets: Double Attention Networks" introduces an innovative component designed to enhance convolutional neural networks (CNNs) by capturing long-range dependencies more efficiently. This component, referred to as the "double attention block," is proposed as a solution to the inherent limitations of conventional CNNs in modeling relationships over extensive spatial and temporal ranges within image and video data.

Methodology and Architecture

Traditional CNNs struggle to effectively grasp global interdependencies due to their focus on local feature extraction. The proposed $A^2$-Nets overcome this by employing a double attention mechanism. This mechanism consists of two primary steps: feature gathering through second-order attention pooling, followed by adaptive feature distribution.

1. Feature Gathering: The first attention mechanism aggregates significant features from the entire input space using second-order statistics, rather than relying solely on first-order pooling methods like average or max pooling. This approach captures complex feature correlations to better encode global information.
2. Feature Distribution: The subsequent step involves distributing the gathered features to each spatial-temporal location. This distribution is adaptive, tailored to the specific features at each location, thereby facilitating the learning of intricate relationships without substantially increasing the model's depth or computational load.

This novel block can be seamlessly integrated into existing network architectures, providing an efficient means of enhancing their capacity to learn from global context.

Experimental Insights

The experiments conducted demonstrate the effectiveness of $A^2$-Nets on various benchmarks, including image and video recognition tasks. Specifically, the integration of the double attention block in a ResNet-50 architecture outperformed a larger ResNet-152 model on the ImageNet-1k dataset, achieving these results with over 40% fewer parameters and lower FLOPs.

For video recognition, the paper illustrates that the $A^2$-Nets achieve state-of-the-art performance on datasets such as Kinetics and UCF-101. The experimental outcomes indicate superior accuracy and efficiency compared to notable models like I3D and R(2+1)D, with significant improvements in computational efficiency.

Implications and Future Work

The introduction of $A^2$-Nets has significant implications for both theoretical research and practical applications. The ability to model long-range dependencies more efficiently can lead to advancements in areas such as real-time video analysis, where reduced computational costs are crucial.

Future research directions could explore further optimization of the double attention block and its integration with mobile-compatible network architectures. This could enhance the model's effectiveness in resource-constrained environments, broadening its applicability across diverse AI-driven tasks.

In conclusion, $A^2$-Nets represent an impactful contribution to the field of deep learning, particularly in enhancing the global feature extraction capabilities of neural networks while maintaining computational efficiency.