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English(EN) SCMerge: Spatial-Consistency-Aware Token Pair Merging for Efficient Semantic Segmentation

新方法提升视觉Transformer在语义分割中的效率

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了两种新方法来提高视觉Transformer（ViTs）在语义分割任务中的效率。TEAL专注于主动学习，使模型能够识别最具信息量的像素位置进行标注，从而以最少的标注数据实现高性能。SCMerge通过合并相似的令牌并考虑空间一致性来提高效率，提供了准确性和计算成本之间更好的权衡。这两种方法都旨在使基于ViT的语义分割更加实用和具成本效益。 AI

影响 这些方法可以显著降低语义分割的计算成本和数据需求，使先进的视觉Transformer模型在实际应用中更容易获得。

排序理由 两篇研究论文介绍了提高视觉Transformer在语义分割中效率的新颖方法。

在 arXiv cs.CV 阅读 →

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新方法提升视觉Transformer在语义分割中的效率

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两篇研究论文介绍了提高视觉Transformer在语义分割中效率的新颖方法。
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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Utku Cicek, Mahip Singh, Ziyao Shang, Kimathi Kaai, C Thomas, Pedro Pablo Guerrero Vela, Alexander Wong, Sirisha Rambhatla ·

    像素到令牌：低预算语义分割的令牌空间高效主动学习

    arXiv:2610.20881v1 Announce Type: cross Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbones largely operate at the patch or region level, where annotation requirements …

  2. arXiv cs.CV TIER_1 English(EN) · Junfeng Ren (Southern University of Science and Technology), Yi Gong (Southern University of Science and Technology), Yuan Zeng (Shenzhen Technology University) ·

    SCMerge：用于高效语义分割的空间一致性感知令牌对合并

    arXiv:2610.21642v1 Announce Type: new Abstract: Vision Transformer architectures have showcased impressive performance on semantic segmentation, yet their quadratic attention complexity hinders practical applications. Recent works have focused on merging tokens to reduce sequence…

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