Children learn to build a visual representation of the world from
unsupervised exploration and we hypothesize that a key part of this learning
ability is the use of self-generated navigational information as a similarity
label to drive a learning objective for self-supervised learning. The goal of
this work is to exploit navigational information in a visual environment to
provide performance in training that exceeds the state-of-the-art
self-supervised training. Here, we show that using spatial and temporal
information in the pretraining stage of contrastive learning can improve the
performance of downstream classification relative to conventional contrastive
learning approaches that use instance discrimination to discriminate between
two alterations of the same image or two different images. We designed a
pipeline to generate egocentric-vision images from a photorealistic ray-tracing
environment (ThreeDWorld) and record relevant navigational information for each
image. Modifying the Momentum Contrast (MoCo) model, we introduced spatial and
temporal information to evaluate the similarity of two views in the pretraining
stage instead of instance discrimination. This work reveals the effectiveness
and efficiency of contextual information for improving representation learning.
The work informs our understanding of the means by which children might learn
to see the world without external supervision.

本研究旨在利用空间和时间的信息来改善自我监督学习，以提高分类性能。我们设计了一种流程来生成来自 ThreeDWorld 的自我视觉图像并记录相关的导航信息。修改动量对比（MoCo）模型，以代替实例区分进行预训练阶段中两个视图的相似性评估。结果显示，对比实例区分方法，本方法优于自我监督训练状态下的性能.