Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks

Abstract: While the use of bottom-up local operators in convolutional neural networks (CNNs) matches well some of the statistics of natural images, it may also prevent such models from capturing contextual long-range feature interactions. In this work, we propose a simple, lightweight approach for better context exploitation in CNNs. We do so by introducing a pair of operators: gather, which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features. The operators are cheap, both in terms of number of added parameters and computational complexity, and can be integrated directly in existing architectures to improve their performance. Experiments on several datasets show that gather-excite can bring benefits comparable to increasing the depth of a CNN at a fraction of the cost. For example, we find ResNet-50 with gather-excite operators is able to outperform its 101-layer counterpart on ImageNet with no additional learnable parameters. We also propose a parametric gather-excite operator pair which yields further performance gains, relate it to the recently-introduced Squeeze-and-Excitation Networks, and analyse the effects of these changes to the CNN feature activation statistics.

• The paper introduces gather-excite operators that aggregate and redistribute features to enhance CNNs' understanding of global contextual information.
• It shows that a ResNet-50 augmented with these operators outperforms the deeper ResNet-101 on ImageNet, demonstrating efficiency and effectiveness.
• The approach offers a practical, low-overhead solution for improving performance in resource-constrained applications and varied network architectures.

Exploiting Feature Context in Convolutional Neural Networks with Gather-Excite Operators

The paper "Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks" presents an innovative approach to improve contextual feature exploitation in CNNs. Traditional CNNs, while effective in many tasks, often rely on localized operations that may not fully capture long-range contextual information essential for tasks like image classification. This paper addresses this limitation by introducing gather-excite operators which enhance contextual interactions efficiently and with minimal computational overhead.

Key Contributions

The primary contribution of this work is the introduction of the gather and excite operators:

1. Gather Operator: It aggregates feature responses from broad spatial regions, providing CNNs with a more holistic representation of input features.
2. Excite Operator: It redistributes aggregated information back into the local features, modulating them with the gathered contextual insights.

These operators are lightweight, meaning they add minimal parameters and computational cost to existing architectures. Impressively, integrating these operators can match or even exceed the performance of significantly deeper models, as evidenced by the enhanced performance of a ResNet-50 augmented with these operators over its deeper counterpart, ResNet-101, on the ImageNet dataset.

Experimental Results

The paper performs rigorous experiments across several datasets to validate the efficacy of the gather-excite framework. Key results include:

• A ResNet-50 with gather-excite operators achieved superior accuracy compared to a ResNet-101, highlighting the efficiency of context exploitation.
• The parametric variant of the operators further improved performance, showcasing the potential for substantial model enhancements with minimal architectural changes.
• The framework demonstrated versatility and applicability across different network depths and architectures, indicating its broad utility in various contexts.

Theoretical and Practical Implications

The implications of this work extend across both theoretical and practical domains:

• Theoretical: The paper offers insights into the benefits of feature context aggregation in deep networks, advancing the understanding of how CNN architectures can be augmented for improved feature interactions without heavily increasing computational demands.
• Practical: By enhancing existing models with minimal overhead, gather-excite operators provide a practical solution for deploying efficient and high-performing models in resource-constrained environments, such as mobile devices.

Future Directions

Looking ahead, this research opens several avenues for exploration in AI and computer vision:

• Integration with Other Architectures: Extending gather-excite operators to other state-of-the-art architectures could yield further insights and improvements.
• Task-Specific Adaptations: Tailoring these operators for specific tasks like semantic segmentation or object detection could lead to enhanced performance in these domains as well.
• Interpretability and Feature Analysis: A deeper investigation into the role of these operators in shaping the learned feature representations could contribute to the interpretability of CNNs.

In conclusion, the gather-excite framework represents a significant advancement in neural network design by efficiently leveraging feature context. This work stands as a testament to the potential for incremental yet impactful innovations in machine learning and computer vision.