ARTA: Adaptive Mixed-Resolution Token Allocation for Efficient Dense Feature Extraction
Source: arXiv:2603.26258v1
Type: Cross
Abstract
We present ARTA, a mixed-resolution coarse-to-fine vision transformer designed for efficient dense feature extraction. Unlike traditional models that initiate the process with dense high-resolution (fine) tokens, ARTA innovatively starts with low-resolution (coarse) tokens. This shift allows for a lightweight allocator to intelligently predict which regions necessitate an increased number of fine tokens. The allocator works iteratively, assessing semantic (class) boundary scores and allocating additional tokens to patches that surpass a predefined low threshold. This approach effectively concentrates token density around semantic boundaries while maintaining a high sensitivity to weak boundary evidence.
Key Features of ARTA
- Coarse-to-Fine Approach: Initiates with low-resolution tokens to reduce initial computational load.
- Lightweight Allocator: Predicts regions needing more detailed representation, enhancing efficiency.
- Semantic Boundary Focus: Concentrates token allocation near boundaries, promoting accurate class representation.
- Mixed-Resolution Attention: Facilitates interactions between coarse and fine tokens, optimizing computation where necessary.
Performance Highlights
Experiments conducted on multiple benchmark datasets demonstrate that ARTA achieves state-of-the-art results on both ADE20K and COCO-Stuff while significantly lowering the number of floating-point operations per second (FLOPs). Additionally, ARTA delivers competitive performance on the Cityscapes dataset with markedly reduced computational requirements. For instance, the ARTA-Base model registers an impressive 54.6 mean Intersection over Union (mIoU) on the ADE20K dataset within the ~100M-parameter class. This performance is achieved while utilizing fewer FLOPs and less memory compared to similar backbone models.
Conclusion
ARTA presents a groundbreaking methodology in the domain of vision transformers by emphasizing an adaptive mixed-resolution token allocation system. This innovative approach not only improves computational efficiency but also enhances the quality of dense feature extraction. As the demand for high-performance models continues to grow in the field of computer vision, ARTA sets a new benchmark for future research and applications.
Future Directions
Looking ahead, the development team plans to further refine the ARTA framework by exploring additional features and optimizations. Potential avenues for future research may include:
- Integrating advanced machine learning techniques to enhance the allocator’s predictive capabilities.
- Assessing ARTA’s applicability to real-time systems and edge computing environments.
- Exploring the use of ARTA in diverse domains such as medical imaging and autonomous vehicles.
In summary, ARTA represents a significant advancement in the efficient extraction of dense features in computer vision, paving the way for future innovations in the field.