VSA: Learning Varied-Size Window Attention in Vision Transformers (2204.08446v2)

Abstract: Attention within windows has been widely explored in vision transformers to balance the performance, computation complexity, and memory footprint. However, current models adopt a hand-crafted fixed-size window design, which restricts their capacity of modeling long-term dependencies and adapting to objects of different sizes. To address this drawback, we propose \textbf{V}aried-\textbf{S}ize Window \textbf{A}ttention (VSA) to learn adaptive window configurations from data. Specifically, based on the tokens within each default window, VSA employs a window regression module to predict the size and location of the target window, i.e., the attention area where the key and value tokens are sampled. By adopting VSA independently for each attention head, it can model long-term dependencies, capture rich context from diverse windows, and promote information exchange among overlapped windows. VSA is an easy-to-implement module that can replace the window attention in state-of-the-art representative models with minor modifications and negligible extra computational cost while improving their performance by a large margin, e.g., 1.1\% for Swin-T on ImageNet classification. In addition, the performance gain increases when using larger images for training and test. Experimental results on more downstream tasks, including object detection, instance segmentation, and semantic segmentation, further demonstrate the superiority of VSA over the vanilla window attention in dealing with objects of different sizes. The code will be released https://github.com/ViTAE-Transformer/ViTAE-VSA.

Insightful Overview of "VSA: Learning Varied-Size Window Attention in Vision Transformers"

The paper "VSA: Learning Varied-Size Window Attention in Vision Transformers" introduces an innovative approach to enhance the adaptability and performance of vision transformers. This paper proposes a novel attention mechanism called Varied-Size Window Attention (VSA) aimed primarily at overcoming the limitations of fixed-size window designs in vision transformers. By facilitating a more flexible and adaptive approach to window attention, VSA captures richer contextual information and effectively models long-term dependencies crucial for object recognition tasks.

Technical Contribution

The VSA mechanism tackles two primary constraints of existing window-based attention models: the inability to model long-range dependencies and suboptimal performance due to fixed window sizes. VSA introduces a window regression module that predicts the size and location of attention windows based on input data. This allows the adaptation of window sizes to better suit objects of various scales. VSA is employed independently for each attention head, enabling significant performance improvements with minimal modifications.

The implementation is computationally efficient, retaining the linear complexity of the original window attention models while significantly enhancing performance metrics across several vision tasks. Specifically, replacing the window attention with VSA results in substantial improvements in accuracy, such as a noteworthy 1.1% Top-1 accuracy boost for the Swin-T model on ImageNet classification tasks.

Key Results and Claims

The empirical findings from this research are considerable. The paper demonstrates quantitative performance enhancements across multiple tasks:

• Image Classification: Implementing VSA showed discernible improvement over comparable models in ImageNet classification, such as a notable increase from 81.2% to 82.3% in Top-1 accuracy for Swin-T.
• Object Detection and Instance Segmentation: Substantial gains were observed in mean Average Precision (mAP) when integrating VSA with standard frameworks like Mask RCNN, with enhancements reaching up to 2.4 mAP.
• Semantic Segmentation: On the Cityscapes dataset, VSA outperformed traditional window attention mechanisms with an increase in mIoU, attesting to the varied-size attention's capability in dense prediction tasks.

The paper highlights that VSA's ability to dynamically change window sizes is particularly advantageous when operating under resolutions higher than the typical 224×224 input size, aligning attention regions more effectively with large objects.

Implications and Future Directions

The implications of this research extend into practical applications of vision transformers across various scales and tasks. The proposed adaptive mechanism promotes a universal applicability across vision-related fields, enhancing the ability to handle diverse image resolutions and object scales efficiently. This advancement can significantly drive progress in real-world applications like autonomous driving, video surveillance, and robotic vision where dynamic object sizes are prevalent.

Looking forward, the exploration of VSA in conjunction with other multi-head self-attention variants, such as cross-shaped attention as seen in CSwin or attention mechanisms that incorporate deformable convolutions, presents an interesting avenue for future research. Additionally, investigating more efficient token sampling methods could further optimize VSA, particularly for extremely large attention windows.

In conclusion, the introduction of Varied-Size Window Attention in vision transformers showcases a meaningful step toward more flexible and capable deep learning architectures. This work lays a foundation for continued innovation, offering insights that could catalyze future developments in adaptive attention mechanisms and beyond.