AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers

• URL: http://arxiv.org/abs/2605.27465v1
• Date: Tue, 26 May 2026 05:35:39 GMT
• Title: AdaMerge: Salience-Aware Adaptive Token Merging for Training-Free Acceleration of Vision Transformers
• Abstract summary: AdaMerge is a token-merging framework based on two complementary mechanisms.<n>It consistently outperforms ToMe, PiToMe, and DSM across all FLOPs-matched regimes.
• Score: 5.739405014622565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 token-merging framework based on two complementary mechanisms. First, salience-weighted similarity leverages column-wise feature-affinity centrality as a token-importance proxy and incorporates the resulting salience scores into the bipartite matching score, ensuring that pivotal tokens contribute more strongly to the merged representation. Second, adaptive merging intensity uses pre-computed layer-wise similarity statistics to dynamically modulate the per-layer reduction count in accordance with input-specific redundancy. On ImageNet-1k with ViT-B/16, AdaMerge consistently outperforms ToMe, PiToMe, and DSM across all FLOPs-matched regimes. The accuracy gap widens monotonically with compression: at the 13.4G FLOPs operating point, AdaMerge sustains a Top-1 degradation of only -1.06%, compared to -1.45% for PiToMe and -4.62% for DSM. To our knowledge, AdaMerge is the first to combine salience-weighted similarity and adaptive per-layer reduction into a single training-free token merging framework, advancing the accuracy-FLOPs Pareto frontier of ViT acceleration.

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