Efficient Self-supervised Vision Transformers for Representation Learning (2106.09785v2)

Abstract: This paper investigates two techniques for developing efficient self-supervised vision transformers (EsViT) for visual representation learning. First, we show through a comprehensive empirical study that multi-stage architectures with sparse self-attentions can significantly reduce modeling complexity but with a cost of losing the ability to capture fine-grained correspondences between image regions. Second, we propose a new pre-training task of region matching which allows the model to capture fine-grained region dependencies and as a result significantly improves the quality of the learned vision representations. Our results show that combining the two techniques, EsViT achieves 81.3% top-1 on the ImageNet linear probe evaluation, outperforming prior arts with around an order magnitude of higher throughput. When transferring to downstream linear classification tasks, EsViT outperforms its supervised counterpart on 17 out of 18 datasets. The code and models are publicly available: https://github.com/microsoft/esvit

• The paper presents a multi-stage architecture with sparse self-attention to reduce complexity while preserving effective feature learning.
• The paper introduces a novel region-matching pre-training task that enhances fine-grained image dependency capture.
• The paper demonstrates that EsViT achieves 81.3% top-1 accuracy on ImageNet and outperforms supervised methods on 17 out of 18 downstream tasks.

Efficient Self-supervised Vision Transformers for Representation Learning

This paper introduces an approach to enhance the efficiency of self-supervised learning in vision transformers, specifically through the development of Efficient Self-supervised Vision Transformers (EsViT). The research identifies two principal techniques that significantly contribute to better visual representation learning: the adoption of multi-stage architectures with sparse self-attention mechanisms and the introduction of a region-matching pre-training task.

Key Contributions

1. Multi-stage Architecture with Sparse Self-attention: The paper conducts a thorough empirical paper demonstrating that employing multi-stage architectures with sparse self-attentions substantially reduces modeling complexity. However, this simplification comes at the cost of losing the ability to capture detailed correspondences between image regions. This trade-off is critical in maintaining the efficiency and scalability of vision transformers without sacrificing the quality of learned features.
2. Region Matching for Pre-training: To mitigate the loss from the multi-stage approach, the authors propose a novel pre-training task focused on region matching. This task enhances the model's capacity to discern fine-grained region dependencies within images. By integrating this task, the EsViT model significantly improves the quality of the visual representations learned.

Experimental Results and Observations

• The EsViT model achieves an impressive 81.3% top-1 accuracy on the ImageNet linear probe evaluation. This performance surpasses previous models, demonstrating higher throughput efficiency by an approximate order of magnitude.
• When applied to downstream linear classification tasks, EsViT outperforms its supervised counterparts on 17 out of 18 datasets tested.

These results underline the effectiveness of combining multi-stage architectures with region matching in enhancing the representation capabilities of vision transformers.

Implications and Future Directions

The development of EsViT has notable implications for both theoretical and practical domains in AI:

• Theoretical Impact: The findings contribute to a deeper understanding of how sparse attention and structural learning mechanisms affect feature quality in transformers. This knowledge is pivotal for further advancements in self-supervised learning paradigms.
• Practical Impact: By reducing computational demands while increasing performance, EsViT offers a feasible approach to deploying efficient visual recognition systems in resource-constrained environments.

Future research directions could involve exploring the integration of EsViT in varied contexts and applications, including real-time visual recognition tasks and enhanced scalability to even larger datasets. Furthermore, investigations into alternative architectures that complement the region-matching pre-training could yield additional improvements in transformer efficiency and accuracy.

Conclusion

The paper presents significant advancements in the field of self-supervised vision transformers. By addressing critical challenges in model efficiency and region correspondence capture, EsViT sets a notable precedent for future AI research. The open release of the code and models further encourages the community to build upon these insights, facilitating progressive developments in self-supervised learning techniques.