Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, Thomas B. Moeslund
{"title":"聚类令牌聚类","authors":"Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, Thomas B. Moeslund","doi":"arxiv-2409.11923","DOIUrl":null,"url":null,"abstract":"We present Agglomerative Token Clustering (ATC), a novel token merging method\nthat consistently outperforms previous token merging and pruning methods across\nimage classification, image synthesis, and object detection & segmentation\ntasks. ATC merges clusters through bottom-up hierarchical clustering, without\nthe introduction of extra learnable parameters. We find that ATC achieves\nstate-of-the-art performance across all tasks, and can even perform on par with\nprior state-of-the-art when applied off-the-shelf, i.e. without fine-tuning.\nATC is particularly effective when applied with low keep rates, where only a\nsmall fraction of tokens are kept and retaining task performance is especially\ndifficult.","PeriodicalId":501130,"journal":{"name":"arXiv - CS - Computer Vision and Pattern Recognition","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Agglomerative Token Clustering\",\"authors\":\"Joakim Bruslund Haurum, Sergio Escalera, Graham W. Taylor, Thomas B. Moeslund\",\"doi\":\"arxiv-2409.11923\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present Agglomerative Token Clustering (ATC), a novel token merging method\\nthat consistently outperforms previous token merging and pruning methods across\\nimage classification, image synthesis, and object detection & segmentation\\ntasks. ATC merges clusters through bottom-up hierarchical clustering, without\\nthe introduction of extra learnable parameters. We find that ATC achieves\\nstate-of-the-art performance across all tasks, and can even perform on par with\\nprior state-of-the-art when applied off-the-shelf, i.e. without fine-tuning.\\nATC is particularly effective when applied with low keep rates, where only a\\nsmall fraction of tokens are kept and retaining task performance is especially\\ndifficult.\",\"PeriodicalId\":501130,\"journal\":{\"name\":\"arXiv - CS - Computer Vision and Pattern Recognition\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computer Vision and Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.11923\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11923","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.