Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention (2201.01615v4)

Abstract: Multi-scale representations are crucial for semantic segmentation. The community has witnessed the flourish of semantic segmentation convolutional neural networks (CNN) exploiting multi-scale contextual information. Motivated by that the vision transformer (ViT) is powerful in image classification, some semantic segmentation ViTs are recently proposed, most of them attaining impressive results but at a cost of computational economy. In this paper, we succeed in introducing multi-scale representations into semantic segmentation ViT via window attention mechanism and further improves the performance and efficiency. To this end, we introduce large window attention which allows the local window to query a larger area of context window at only a little computation overhead. By regulating the ratio of the context area to the query area, we enable the $\textit{large window attention}$ to capture the contextual information at multiple scales. Moreover, the framework of spatial pyramid pooling is adopted to collaborate with $\textit{the large window attention}$, which presents a novel decoder named $\textbf{la}$rge $\textbf{win}$dow attention spatial pyramid pooling (LawinASPP) for semantic segmentation ViT. Our resulting ViT, Lawin Transformer, is composed of an efficient hierachical vision transformer (HVT) as encoder and a LawinASPP as decoder. The empirical results demonstrate that Lawin Transformer offers an improved efficiency compared to the existing method. Lawin Transformer further sets new state-of-the-art performance on Cityscapes (84.4% mIoU), ADE20K (56.2% mIoU) and COCO-Stuff datasets. The code will be released at https://github.com/yan-hao-tian/lawin

• The paper introduces a large window attention mechanism that efficiently captures multi-scale contextual information with minimal computational overhead.
• The paper presents LawinASPP, a novel decoder inspired by atrous spatial pyramid pooling that enriches semantic segmentation performance.
• The paper demonstrates significant mIoU improvements on benchmarks, achieving 84.4% on Cityscapes and 56.2% on ADE20K, highlighting its practical impact.

Overview of "Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attention"

The paper introduces a novel architecture for semantic segmentation known as the Lawin Transformer, which leverages a multi-scale representation approach via a proposed large window attention mechanism. The design intention is to enhance performance and computational efficiency in semantic segmentation tasks, a domain historically dominated by convolutional neural networks (CNNs). With the increasing utilization of Vision Transformers (ViTs) being impeded by their substantial computational demands, the Lawin Transformer addresses these by integrating an efficient hierarchical vision transformer (HVT) with the newly developed LawinASPP decoder.

Key Contributions

1. Large Window Attention Mechanism: Unlike conventional local window attention which limits contextual information, large window attention allows a patch to query a significantly broader context efficiently. By adjusting the ratio of context to query area, the Lawin Transformer can gather multi-scale contextual information with minimal computational increase. This is achieved by pooling the context patches to maintain computational complexity comparable to local window attention.
2. LawinASPP Architecture: The design is inspired by atrous spatial pyramid pooling (ASPP) and incorporates spatial pyramid pooling (SPP) to form the LawinASPP, which serves as the decoder. This strategy expands the contextual field at multiple scales and improves semantic segmentation by accumulating context-rich representations effectively.
3. Enhanced Empirical Efficiency: Benchmarking on datasets such as Cityscapes, ADE20K, and COCO-Stuff, the Lawin Transformer sets new records in performance with notable improvements in mean intersection-over-union (mIoU). For instance, it achieves 84.4% mIoU on Cityscapes, 56.2% mIoU on ADE20K, and demonstrates consistent superiority over competing architectures such as SegFormer and Swin-UperNet.

Theoretical and Practical Implications

The paper's contributions provide significant advancements in Vision Transformer architectures by furnishing a path to integrating more comprehensive contextual information without disproportionately increasing computation loads. This is particularly significant in applications demanding real-time performance coupled with high accuracy, such as autonomous driving and complex scene understanding.

Theoretical implications include potential developments in attention mechanisms that could dynamically adjust context queries based on target complexity. The methodology might be extended to other domains where capturing scalable contextual relations is crucial, such as video or sequential data interpretation.

Future Directions

The research opens several avenues for further exploration:

• Dynamic Context Querying: Investigating adaptive mechanisms for real-time scaling of context window sizes based on image content complexity might improve resource use further.
• Broader Applications: Applying Lawin Transformer's principles to non-vision tasks where attention-based models thrive—such as in natural language processing—might reveal unforeseen benefits.
• Hybrid Architectures: Combining the efficiency of large window attention with neural architectural search could yield new models that balance traditional CNN strengths with transformer advantages effectively.

Overall, the Lawin Transformer highlights a pivotal step forward for semantic segmentation methodologies, providing an advanced framework for incorporating extensive and efficient contextual understanding into transformer-based architectures. This work is expected to stimulate further research into multispectral attention mechanisms and their deployment across broader AI model ecosystems.