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English(EN) Token-Space Mask Prediction for Efficient Vision Transformer Segmentation

TokenMask 提升视觉Transformer分割效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了TokenMask，一种新颖的视觉Transformer分割方法，无需显式的图像空间重建。该方法直接从查询-Token亲和度计算掩码logits，简化了计算结构并提高了效率。TokenMask在各种数据集和骨干网络上展示了具有竞争力的准确性，同时降低了计算和内存需求，使其适用于嵌入式视觉系统。 AI

影响 引入了一种更高效的视觉Transformer分割方法，有望在边缘设备上实现更快、更易于部署的AI系统。

排序理由 关于视觉Transformer分割新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

TokenMask 提升视觉Transformer分割效率

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关于视觉Transformer分割新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · François Goulette ·

    面向高效 Vision Transformer 分割的 Token-Space Mask 预测

    Query-based Vision Transformer segmentation models typically reconstruct dense spatial feature maps to predict masks, inheriting design patterns from convolutional architectures. We show that this explicit image-space reconstruction is not required. We introduce TokenMask, a toke…

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