显式构建空间语义表示，让模型更轻更快地定位物体
WorldComp2D: Spatio-semantic Representations of Object Identity and Location from Local Views
- 用多尺度局部感受野显式设计潜在空间结构
- 参数量和计算量分别减少4.0倍和2.2倍
- 适合需要实时推理的轻量化视觉任务
学习能同时捕捉语义与空间信息的潜在表示，是高效时空语义推理的核心。然而，现有方法多依赖隐式潜在结构结合密集特征图或任务特定头，限制了计算效率与灵活性。我们提出WorldComp2D，一种新型轻量级表示学习框架，通过多尺度局部感受野，显式依据物体身份与空间邻近性组织潜在空间几何结构。该框架包含：(i) 依赖邻近性的编码器，将观测映射到时空语义潜在空间；(ii) 定位器，从生成的时空语义表示中推断物体在输入中的坐标。以人脸关键点定位为例，相比当前最先进轻量模型，WorldComp2D在保持CPU实时性能的前提下，参数量减少最多4.0倍，计算量减少最多2.2倍。结果表明，显式结构化的潜在空间为时空语义推理提供了高效且通用的基础。代码已开源：https://github.com/JinSeongmin/WorldComp2D。
原文摘要 · Abstract (English)
Learning latent representations that capture both semantic and spatial information is central to efficient spatio-semantic reasoning. However, many existing approaches rely on implicit latent structures combined with dense feature maps or task-specific heads, limiting computational efficiency and flexibility. We propose WorldComp2D, a novel lightweight representation learning framework that explicitly structures latent space geometry according to object identity and spatial proximity using multiscale local receptive fields. This framework consists of (i) a proximity-dependent encoder that maps a given observation into a spatio-semantic latent space and (ii) a localizer that infers the coordinates of objects in the input from the resulting spatio-semantic representation. Using facial landmark localization as a proof-of-concept, we show that, compared to SoTA lightweight models, WorldComp2D reduces the numbers of parameters and FLOPs by up to 4.0X and 2.2X, respectively, while maintaining real-time performance on CPU. These results demonstrate that explicitly structured latent spaces provide an efficient and general foundation for spatio-semantic reasoning. This framework is open-sourced at https://github.com/JinSeongmin/WorldComp2D.
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