Slow Perception: Let's Perceive Geometric Figures Step-by-step
Haoran Wei, Youyang Yin, Yumeng Li, Jia Wang, Liang Zhao, Jianjian, Sun, Zheng Ge, Xiangyu Zhang, Daxin Jiang

TL;DR
This paper introduces 'slow perception' for geometric figures, guiding models to gradually understand and reconstruct complex shapes step-by-step, improving perception accuracy in visual reasoning tasks.
Contribution
It proposes a novel 'slow perception' framework with perception decomposition and flow stages, mimicking human-like step-by-step geometric understanding for better visual reasoning.
Findings
Slow perception improves geometric figure copying accuracy.
Inference time scales linearly with perception quality.
Step-by-step perception enhances understanding of complex shapes.
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…
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Taxonomy
TopicsData Visualization and Analytics
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
