# Agglomerative Token Clustering

Source: [Agglomerative Token Clustering](<https://www.sota2.com/research/paper/68fa74d81e209131cea64169>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:19:27.756Z

Authors: Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, Thomas B. Moeslund

Published: 2024-09-18T12:37:58.000Z

## Abstract

We present Agglomerative Token Clustering (ATC), a novel token merging method that consistently outperforms previous token merging and pruning methods across image classification, image synthesis, and object detection & segmentation tasks. ATC merges clusters through bottom-up hierarchical clustering, without the introduction of extra learnable parameters. We find that ATC achieves state-of-the-art performance across all tasks, and can even perform on par with prior state-of-the-art when applied off-the-shelf, i.e. without fine-tuning. ATC is particularly effective when applied with low keep rates, where only a small fraction of tokens are kept and retaining task performance is especially difficult.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2409.11923>)

[PDF](<https://arxiv.org/pdf/2409.11923.pdf>)