Positional Encodings Anchor Spatial Structure in Vision Transformers: A Geometric Perspective on Robustness
arXiv:2606.00124v1 Announce Type: cross Abstract: Positional embeddings (PEs) in Vision Transformers (ViTs) are known to impact performance and robustness, but their role in shaping internal spatial representations is not well understood. In this work, we study how different forms of PEs influence the representational geometry of ViTs and how these changes relate to robustness under content-disrupting distribution shifts. We introduce a metric, the Spatial Similarity Distance Correlation (SSDC), to quantify spatial structure in token representations. Using this metric, we show that ViTs traine
The rapid advancement and widespread adoption of Vision Transformers necessitate a deeper understanding of their underlying mechanisms, especially as they become critical components in many AI applications.
Understanding how positional encodings influence ViT robustness is crucial for developing more reliable and deployable AI systems, particularly in sensitive applications where distributional shifts are common.
This research provides a new metric and geometric perspective for analyzing ViT robustness, potentially leading to the design of more resilient AI architectures and improved performance under varied conditions.
- · AI researchers and developers
- · Companies building robust AI systems
- · Computer vision applications
- · AI systems with poor robustness
- · Legacy computer vision models
Improved understanding of Vision Transformer internal workings and robustness factors.
Development of new ViT architectures specifically designed for enhanced robustness to distribution shifts.
Accelerated deployment of AI in mission-critical applications requiring high reliability across diverse operating environments.
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Read at arXiv cs.LG