AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers
arXiv:2605.27465v1 Announce Type: cross Abstract: The quadratic cost of self-attention in Vision Transformers (ViTs) constitutes a fundamental bottleneck for practical deployment, motivating a vibrant line of research on token reduction. Among existing approaches, token merging (ToMe) has emerged as an elegant training-free solution; yet its design rests on an unspoken premise of token equality, which contravenes the well-documented non-uniformity of self-attention and leads to information loss in high-salience tokens under aggressive compression. We address this limitation with AdaMerge, a to
The quadratic computational cost of Vision Transformers (ViTs) is a current practical bottleneck, driving immediate research for efficiency improvements.
Improving the efficiency of Vision Transformers through methods like AdaMerge directly reduces the computational burden, making advanced AI models more accessible and deployable.
Vision Transformer models can now be accelerated significantly without additional training, potentially lowering operational costs and enabling broader application in resource-constrained environments.
- · AI compute providers
- · Edge AI developers
- · Computer vision researchers
- · Industries deploying visual AI
- · Inefficient AI model architectures
- · Developers reliant on brute-force compute scaling
Wider adoption of Vision Transformers due to reduced computational requirements.
Increased demand for specialized hardware optimized for efficient ViT operations, rather than raw FLOPs.
Proliferation of vision-based AI applications in embedded systems and real-time scenarios previously unfeasible.
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Read at arXiv cs.AI