聚类令牌聚类

Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, Thomas B. Moeslund
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引用次数: 0

摘要

我们提出了标记聚合法(Agglomerative Token Clustering,ATC),这是一种新颖的标记合并方法,在图像分类、图像合成以及物体检测与分割任务中,其性能始终优于之前的标记合并和剪枝方法。ATC 通过自下而上的分层聚类来合并集群,无需引入额外的可学习参数。我们发现 ATC 在所有任务中都达到了最先进的性能,甚至在现成应用(即不进行微调)的情况下,其性能可以与之前最先进的技术相媲美。ATC 在低保留率情况下的应用尤其有效,在这种情况下,只有一小部分标记被保留,保持任务性能尤其困难。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Agglomerative Token Clustering
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.
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