RegionViT: Regional-to-Local Attention for Vision Transformers (2106.02689v3)

Abstract: Vision transformer (ViT) has recently shown its strong capability in achieving comparable results to convolutional neural networks (CNNs) on image classification. However, vanilla ViT simply inherits the same architecture from the natural language processing directly, which is often not optimized for vision applications. Motivated by this, in this paper, we propose a new architecture that adopts the pyramid structure and employ a novel regional-to-local attention rather than global self-attention in vision transformers. More specifically, our model first generates regional tokens and local tokens from an image with different patch sizes, where each regional token is associated with a set of local tokens based on the spatial location. The regional-to-local attention includes two steps: first, the regional self-attention extract global information among all regional tokens and then the local self-attention exchanges the information among one regional token and the associated local tokens via self-attention. Therefore, even though local self-attention confines the scope in a local region but it can still receive global information. Extensive experiments on four vision tasks, including image classification, object and keypoint detection, semantics segmentation and action recognition, show that our approach outperforms or is on par with state-of-the-art ViT variants including many concurrent works. Our source codes and models are available at https://github.com/ibm/regionvit.

RegionViT: Regional-to-Local Attention for Vision Transformers

The paper introduces an innovative approach to Vision Transformers (ViTs) by proposing RegionViT, which modifies the traditional transformer architecture to better suit the requirements of vision-based tasks. This model capitalizes on the inherent hierarchical structure of visual data by implementing a pyramid structure with regional-to-local attention, diverging from the global self-attention strategy typical of classical transformers developed for NLP tasks.

Theoretical and Methodological Contributions

The traditional ViTs, though successful, rely heavily on architectures borrowed directly from NLP, which might not fully exploit the characteristics of visual data. The proposed RegionViT innovates in this space by tailoring the attention mechanism specifically for vision-based applications. Key features include:

• Regional Token Generation: Unlike global self-attention that processes the entire image uniformly, RegionViT introduces regional tokens derived from various patch sizes. This reflects a multi-scale approach, where each regional token corresponds to general areas of the image, promoting efficiency by curtailing the scope of information processed at any given time.
• Regional-to-Local Attention Mechanism: This process entails two pivotal steps:
  1. Regional Self-Attention: Initially, the model identifies interconnections among regional tokens, enriching them with global information across a sparse scale.
  2. Local Self-Attention: Subsequently, the model narrows the focus, aligning the analysis to each region's associated local tokens, facilitating precise information exchange within defined local segments, thus preserving local details.

Empirical Evaluation and Results

The proposed RegionViT was rigorously evaluated across four prominent vision tasks: image classification, object detection, keypoint detection, semantic segmentation, and action recognition. The empirical results evidenced its superior or comparable performance relative to existing state-of-the-art ViT variants, establishing RegionViT not only as a competitive alternative but also as an architecture attuned to vision specifics.

• Image Classification: RegionViT achieved unparalleled or equitable accuracy levels compared to contemporary transformers, highlighting its efficacy in discerning complex visual patterns.

• Object and Keypoint Detection: The detection tasks underscored RegionViT's robust capability in recognizing and localizing objects within images, a compelling feat in advancing semantic understanding in automated vision systems.
• Semantic Segmentation and Action Recognition: These tasks presented RegionViT's aptitude in delineating intricate visual scenes and identifying human activities, evidencing its broad applicability across various vision domains.

Implications and Future Developments

This research underscores a paradigm shift towards more specialized transformer models that align closer to the data type they process. RegionViT's success prompts further exploration into region-based attention mechanisms, leveraging their potential for honing precision and computational efficiency in vision applications. The model signifies a step towards rendering transformers more versatile across multiple dimensions of visual understanding.

Future research directions may involve enhancing the scalability of RegionViT architectures, devising mechanisms for more efficient regional token generation, and exploring its extensive capabilities in domains requiring high-dimensional pattern recognition, such as healthcare imaging and autonomous navigation.

In conclusion, RegionViT stands out as a tailored solution within the landscape of vision transformers; an exemplar of adaptive innovation steering attention mechanisms to align with the structural nuances of visual data. This development promises substantial contributions to the enhancement of computational models handling complex visual information.