ClustViT paper introduces token merging for efficient semantic segmentation

Researchers have introduced ClustViT, a novel approach to enhance Vision Transformers for semantic segmentation tasks. This method employs a trainable Cluster module to merge similar tokens, guided by segmentation masks, thereby reducing computational complexity. A subsequent Regenerator module restores fine details, enabling faster inference and fewer GFLOPs with comparable accuracy on various datasets. AI

影响 Reduces computational cost for semantic segmentation models, potentially enabling wider use in resource-constrained environments like robotics.

排序理由 This is a research paper detailing a new method for improving Vision Transformers for semantic segmentation.

在 arXiv cs.CV 阅读 →

• arXiv
• ClustViT
• Fabio Montello
• Hugging Face
• Vision Transformer

• 论文
• 基础设施

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