Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets.

翻译：视觉学习通常发生在特定情境中，智能体通过在一致环境中的探索与位置追踪获取技能。智能体的历史空间上下文为自监督对比学习提供了相似性信号。我们提出一种独特方法——环境空间相似性（ESS），用于补充现有对比学习方法。以模拟照片级真实环境的图像作为实验场景，我们证明ESS优于传统实例判别方法。此外，从相同环境中采样额外数据可显著提升准确率并提供新的数据增强手段。ESS在房间分类与空间预测任务中展现出卓越能力，尤其是在陌生环境中。该学习范式有望使智能体在具有独特视觉特征的新环境中实现快速视觉学习。其潜在变革性应用涵盖机器人技术至太空探索领域。我们的概念验证表明，相较于依赖大规模非关联数据集的方法，ESS具有更高的效率。