Slow Perception: Let's Perceive Geometric Figures Step-by-step

Haoran Wei, Youyang Yin, Yumeng Li, Jia Wang, Liang Zhao +4 more
12/30/2024
cs.CV

No official code found. Best implementation: Ucas-HaoranWei/Slow-Perception (163 stars, last commit 2025-07-28).

Abstract

Recently, "visual o1" began to enter people's vision, with expectations that this slow-thinking design can solve visual reasoning tasks, especially geometric math problems. However, the reality is that current LVLMs (Large Vision Language Models) can hardly even accurately copy a geometric figure, let alone truly understand the complex inherent logic and spatial relationships within geometric shapes. We believe accurate copying (strong perception) is the first step to visual o1. Accordingly, we introduce the concept of "slow perception" (SP), which guides the model to gradually perceive basic point-line combinations, as our humans, reconstruct complex geometric structures progressively. There are two-fold stages in SP: a) perception decomposition. Perception is not instantaneous. In this stage, complex geometric figures are broken down into basic simple units to unify geometry representation. b) perception flow, which acknowledges that accurately tracing a line is not an easy task. This stage aims to avoid "long visual jumps" in regressing line segments by using a proposed "perceptual ruler" to trace each line stroke-by-stroke. Surprisingly, such a human-like perception manner enjoys an inference time scaling law -- the slower, the better. Researchers strive to speed up the model's perception in the past, but we slow it down again, allowing the model to read the image step-by-step and carefully.

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Official code implementation of Slow Perception:Let's Perceive Geometric Figures Step-by-step

1638PythonDec 23, 20241 years ago

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Cite this paper

@article{wei2024slow,
  title  = {Slow Perception: Let's Perceive Geometric Figures Step-by-step},
  author = {Haoran Wei and Youyang Yin and Yumeng Li and Jia Wang and Liang Zhao and Jianjian Sun and Zheng Ge and Xiangyu Zhang and Daxin Jiang},
  year   = {2024},
  eprint = {2412.20631},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2412.20631v2}
}

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