arXiv:2605.22372v1 Announce Type: new Abstract: Vision Transformers (ViTs) face severe computational bottlenecks due to the quadratic complexity of self-attention at high resolutions. Existing token reduction methods rely on local metrics - such as single-layer attention scores - that are inherently vulnerable to the attention sink phenomenon, where uninformative tokens are paradoxically preserved over salient foreground objects. We propose ASAP (Attention Sink Anchored Pruning), a training-free framework that recasts this sink as a feature. Modeling ViT information flow as a Lazy Random Walk,
The continuous push for more efficient and performant AI models, especially ViTs, drives research into overcoming computational bottlenecks.
Improving the efficiency of Vision Transformers can lead to more scalable and deployable AI in resource-constrained environments and complex applications.
This research introduces a novel, training-free method to optimize ViTs by intelligently addressing attention sink phenomena, potentially improving their practical applicability.
- · AI hardware manufacturers
- · Developers of vision-based AI applications
- · Cloud computing providers
- · Inefficient large-scale ViT deployments
More efficient Vision Transformers become feasible for a wider range of applications and devices.
Reduced computational costs for vision AI could accelerate adoption across various industries, from autonomous vehicles to medical imaging.
Increased accessibility and efficiency of advanced vision AI might lead to new unforeseen applications and market disruptions.
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Read at arXiv cs.LG