Slow Perception: Let's Perceive Geometric Figures Step-by-step
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.
6773f578fb77c86d80d4c9fc
Haoran Wei
claimed_verified
2025-02-03T08:16:20.719Z
6436618aeef1f55654a9f458
https://cdn-avatars.huggingface.co/v1/production/uploads/6436618aeef1f55654a9f458/OvxGtuDg2GAFG9As-2hzW.jpeg
Haoran Wei
user
HaoranWei
6773f578fb77c86d80d4c9fd
Youyang Yin
6773f578fb77c86d80d4c9fe
Yumeng Li
6773f578fb77c86d80d4c9ff
Jia Wang
6773f578fb77c86d80d4ca00
Liang Zhao
6773f578fb77c86d80d4ca01
Jianjian Sun
6773f578fb77c86d80d4ca02
Zheng Ge
6773f578fb77c86d80d4ca03
Xiangyu Zhang
Slow Perception: Let's Perceive Geometric Figures Step-by-step
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.
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.
Large Vision Language Models (LVLMs) visual o1 slow perception (SP) perception decomposition perception flow perceptual ruler inference time scaling law