title: Slow Perception: Let's Perceive Geometric Figures Step-by-step
summary: 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.
paper_authors._id: 6773f578fb77c86d80d4c9fc
paper_authors.name: Haoran Wei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-03T08:16:20.719Z
paper_authors._id: 6436618aeef1f55654a9f458
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6436618aeef1f55654a9f458/OvxGtuDg2GAFG9As-2hzW.jpeg
paper_authors.fullname: Haoran Wei
paper_authors.type: user
paper_authors.user: HaoranWei
paper_authors._id: 6773f578fb77c86d80d4c9fd
paper_authors.name: Youyang Yin
paper_authors._id: 6773f578fb77c86d80d4c9fe
paper_authors.name: Yumeng Li
paper_authors._id: 6773f578fb77c86d80d4c9ff
paper_authors.name: Jia Wang
paper_authors._id: 6773f578fb77c86d80d4ca00
paper_authors.name: Liang Zhao
paper_authors._id: 6773f578fb77c86d80d4ca01
paper_authors.name: Jianjian Sun
paper_authors._id: 6773f578fb77c86d80d4ca02
paper_authors.name: Zheng Ge
paper_authors._id: 6773f578fb77c86d80d4ca03
paper_authors.name: Xiangyu Zhang
paper_title: Slow Perception: Let's Perceive Geometric Figures Step-by-step
paper_summary: 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.
paper_ai_summary: A new slow-down perception approach for large vision language models aims to enhance their ability in visual reasoning, particularly in accurately copying and understanding geometric figures by breaking down perception into gradual, human-like stages.
paper_ai_keywords: Large Vision Language Models (LVLMs) visual o1 slow perception (SP) perception decomposition perception flow perceptual ruler inference time scaling law