Squeeze-and-Attention Networks for Semantic Segmentation (1909.03402v4)

Abstract: The recent integration of attention mechanisms into segmentation networks improves their representational capabilities through a great emphasis on more informative features. However, these attention mechanisms ignore an implicit sub-task of semantic segmentation and are constrained by the grid structure of convolution kernels. In this paper, we propose a novel squeeze-and-attention network (SANet) architecture that leverages an effective squeeze-and-attention (SA) module to account for two distinctive characteristics of segmentation: i) pixel-group attention, and ii) pixel-wise prediction. Specifically, the proposed SA modules impose pixel-group attention on conventional convolution by introducing an 'attention' convolutional channel, thus taking into account spatial-channel inter-dependencies in an efficient manner. The final segmentation results are produced by merging outputs from four hierarchical stages of a SANet to integrate multi-scale contexts for obtaining an enhanced pixel-wise prediction. Empirical experiments on two challenging public datasets validate the effectiveness of the proposed SANets, which achieves 83.2% mIoU (without COCO pre-training) on PASCAL VOC and a state-of-the-art mIoU of 54.4% on PASCAL Context.

• The paper introduces SANet, which uses a dual squeeze-and-attention module to enhance both pixel-wise prediction and pixel grouping.
• It outperforms state-of-the-art models with an mIoU of 83.2% on PASCAL VOC and 54.4% on PASCAL Context using EfficientNet-b7.
• Its novel architecture offers promising implications for applications in autonomous driving and medical imaging by improving boundary delineation.

Squeeze-and-Attention Networks for Semantic Segmentation: A Critical Review

The integration of attention mechanisms into neural networks has markedly improved their representational capabilities, particularly in the domain of semantic segmentation. This paper presents a novel architecture, Squeeze-and-Attention Networks (SANet), designed to address some limitations present in existing convolutional-based segmentation networks. The primary innovation within SANet is the squeeze-and-attention (SA) module, which introduces a dual approach to addressing pixel-group attention and pixel-wise prediction, core tasks in semantic segmentation.

Overview of the Methodology

The paper dissects semantic segmentation into two distinct tasks: pixel-wise prediction and pixel grouping. While pixel-wise prediction involves predicting each pixel's label, pixel grouping focuses on ensuring that spatially related pixels are recognized as part of a group. Traditional networks often emphasize pixel-wise prediction, leaving pixel grouping underexplored. SANet endeavors to fill this gap by implementing SA modules that effectively attend to spatial-channel interdependencies.

SA Module Design: The SA module operates through an additional attention convolutional channel that imparts spatial-channel dependencies effectively, bypassing the limitations imposed by the grid structure of traditional convolutional kernels. This module enhances segmentation performance by amalgamating outputs from multiple hierarchical stages within SANet, thus achieving an enriched multi-scale context understanding.

Experimental Validation and Results

The efficacy of SANet was validated on two significant datasets: PASCAL VOC and PASCAL Context. On PASCAL VOC, SANet achieved a mean Intersection over Union (mIoU) of 83.2% without resorting to COCO pre-training, thereby outperforming several state-of-the-art models. Similarly, it set a new benchmark on PASCAL Context with an mIoU of 54.4%, when using EfficientNet-b7 as the backbone.

Implications and Future Directions

The introduction of SANet and its SA modules offers both theoretical and practical implications for the field:

• Theoretical Advancements: SANet's framework challenges the prevalent approach in semantic segmentation by advocating for the simultaneous addressing of pixel-wise and pixel-group tasks. This dichotomous approach could inspire further research into the roles of attention within neural networks and their potential to enhance various computer vision tasks.
• Practical Applications: Real-world applications, especially in autonomous driving and medical imaging, could benefit from the enhanced segmentation quality that SANet affords. The improved pixel grouping can lead to better object boundary delineation, crucial in domain-critical applications.

Reflections and Speculations

While SANet serves as compelling evidence for the utility of attention mechanisms in segmentation tasks, it also sets the stage for new lines of inquiry. Future work could explore scaling SANet to handle larger datasets or more complex scenes. Similarly, further experimentation with different backbone architectures could provide more insights into optimizing the balance between computational efficiency and segmentation performance.

In conclusion, the SANet architecture represents a targeted advancement in semantic segmentation that emphasizes a balanced approach to attention, addressing both pixel-wise precision and pixel grouping. This duality, backed by strong empirical results, marks a meaningful step forward in the enhancement of neural networks for segmentation purposes.