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English(EN) On Counting Graphs for Modal Logic with Counting

LLMSeg 管道使用 LLM 进行图像分割,无需训练

一个名为 LLMSeg 的新的无训练管道已被开发出来,它利用多模态大语言模型 (MLLMs) 进行图像分割。该方法通过将过程分解为三个阶段:定位、B 样条追踪和迭代精炼,绕过了对专用分割架构或特定任务训练的需求。LLMSeg 通过利用现成的模型(如 Gemini-3.7-Flash)展示了与 Segment Anything Model 3 等专用模型相媲美的性能,甚至在某些情况下超越了它们。 AI

影响 利用通用 LLM 的图像分割新方法正在涌现,这可能会减少对专用模型和训练的需求。

排序理由 该集群包含两篇研究论文,一篇关于理论计算复杂性,另一篇关于新的图像分割管道。

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

LLMSeg 管道使用 LLM 进行图像分割,无需训练

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该集群包含两篇研究论文,一篇关于理论计算复杂性,另一篇关于新的图像分割管道。
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报道来源 [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    On Counting Graphs for Modal Logic with Counting

    In this paper, we study the theoretical computational complexity of counting trees and graphs that are classified positively by a graph neural networks, or said equivalently that satisfy a given formula of modal logic with counting. We also study convergence.

  2. arXiv cs.CV TIER_1 English(EN) · Abdullah Hamdi ·

    LLMSeg: Training-Free Text-Prompted Image Segmentation with B-Splines

    arXiv:2610.29409v1 Announce Type: new Abstract: Promptable segmentation models such as SAM 3 achieve remarkable open-vocabulary performance but require dedicated architectures, large-scale annotation engines, and task-specific training. We ask a different question: can a general-…