Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks (1905.09646v2)

Abstract: The Convolutional Neural Networks (CNNs) generate the feature representation of complex objects by collecting hierarchical and different parts of semantic sub-features. These sub-features can usually be distributed in grouped form in the feature vector of each layer, representing various semantic entities. However, the activation of these sub-features is often spatially affected by similar patterns and noisy backgrounds, resulting in erroneous localization and identification. We propose a Spatial Group-wise Enhance (SGE) module that can adjust the importance of each sub-feature by generating an attention factor for each spatial location in each semantic group, so that every individual group can autonomously enhance its learnt expression and suppress possible noise. The attention factors are only guided by the similarities between the global and local feature descriptors inside each group, thus the design of SGE module is extremely lightweight with \emph{almost no extra parameters and calculations}. Despite being trained with only category supervisions, the SGE component is extremely effective in highlighting multiple active areas with various high-order semantics (such as the dog's eyes, nose, etc.). When integrated with popular CNN backbones, SGE can significantly boost the performance of image recognition tasks. Specifically, based on ResNet50 backbones, SGE achieves 1.2\% Top-1 accuracy improvement on the ImageNet benchmark and 1.0$\sim$2.0\% AP gain on the COCO benchmark across a wide range of detectors (Faster/Mask/Cascade RCNN and RetinaNet). Codes and pretrained models are available at https://github.com/implus/PytorchInsight.

• The paper introduces the Spatial Group-wise Enhance (SGE) module that dynamically reweights spatial groups to improve semantic feature representation in CNNs.
• It leverages statistical similarity between global and local features to generate attention factors, yielding a 1.2% Top-1 accuracy boost on ImageNet and up to 2.0% AP increase on COCO.
• The lightweight design of SGE outperforms traditional attention mechanisms like SE and SK, highlighting its efficiency and potential for diverse applications.

Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

Overview

The paper addresses the challenge of semantic feature representation in Convolutional Neural Networks (CNNs) by introducing the Spatial Group-wise Enhance (SGE) module. This module aims to improve the semantic feature learning capabilities of CNNs by dynamically adjusting the importance of sub-features within groups. By generating attention factors for spatial locations in each semantic group, the SGE module enhances the expression of relevant features and suppresses noise.

Methodology

The SGE approach capitalizes on the traditional concept of feature grouping but extends it spatially. Unlike previous methods that primarily focus on channel dimensions, SGE targets spatial dimensions. This spatial enhancement is achieved through:

• Attention Generation: For each group, attention factors are determined by the similarity between global and local feature descriptors. This requires minimal computational overhead as it relies merely on statistical similarities rather than extensive parameter usage.
• Lightweight Design: With almost no extra parameters, the module maintains efficiency. The attention factors effectively suppress noise and highlight critical semantic regions.

The authors claim that despite training with category supervision only, the SGE module effectively enhances multi-region semantics (e.g., facial features on a dog).

Results

Notable results from SGE integration include:

• ImageNet Benchmark: A significant increase of 1.2% in Top-1 accuracy on ResNet50.
• COCO Benchmark: A 1.0-2.0% increase in Average Precision (AP) across various detectors like Faster RCNN and RetinaNet, particularly excelling in small object detection.

These improvements signify that SGE optimally balances semantic diversity and representation capacity.

Experimental Insights

The SGE module demonstrates superior performance compared to contemporary attention mechanisms such as SE and SK modules while maintaining lower parameter and computational demands. Noteworthy outcomes are:

• Visual Verification: Visualization reveals that SGE effectively accentuates the semantic consistency across different object categories and spatial orientations.
• Statistical Analysis: The introduction of SGE notably increases the variance of activation values, implying enhanced feature localization and noise reduction.

Implications

The SGE module's implications are significant in the field of semantic feature learning. Its capacity to autonomously adjust feature importance without relying on extensive additional parameters or computational resources presents an advantageous paradigm for efficient CNN performance enhancement.

Future Directions

The future of AI could explore integrating SGE with emerging neural network architectures beyond classical CNNs. Its benefits could be particularly impactful in domains requiring spatial precision and low latency, such as autonomous vehicles and medical imaging.

Moreover, expanding the SGE framework to incorporate more complex attention mechanisms, while maintaining computational efficiency, could further enhance semantic feature learning.

Conclusion

The SGE module advances the understanding of spatial feature enhancement in grouped convolutional networks. The approach's effectiveness across various benchmarks underscores its potential as a reliable module for improving semantic feature representation in CNNs, providing a pathway for both theoretical advancement and practical application.