MPM: Mutual Pair Merging for Efficient Vision Transformers
Decreasing sequence length is a common way to accelerate transformers, but prior token reduction work often targets classification and reports proxy metrics rather than end-to-end latency. For semantic segmentation, token reduction is further constrained by the need to reconstruct dense, pixel-aligned features, and on modern accelerators the overhead of computing merge maps can erase expected gains. We propose Mutual Pair Merging (MPM), a training-free token aggregation module that forms mutual nearest-neighbor pairs in cosine space, averages each pair, and records a merge map enabling a gather-based reconstruction before the decoder so that existing segmentation heads can be used unchang...
| Authors | Simon Ravé, Pejman Rasti, David Rousseau |
|---|---|
| Primary category | cs.CV |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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