  Recently introduced self-supervised methods for image representation learning
provide on par or superior results to their fully supervised competitors, yet
the corresponding efforts to explain the self-supervised approaches lag behind.
Motivated by this observation, we introduce a novel visual probing framework
for explaining the self-supervised models by leveraging probing tasks employed
previously in natural language processing. The probing tasks require knowledge
about semantic relationships between image parts. Hence, we propose a
systematic approach to obtain analogs of natural language in vision, such as
visual words, context, and taxonomy. Our proposal is grounded in Marr's
computational theory of vision and concerns features like textures, shapes, and
lines. We show the effectiveness and applicability of those analogs in the
context of explaining self-supervised representations. Our key findings
emphasize that relations between language and vision can serve as an effective
yet intuitive tool for discovering how machine learning models work,
independently of data modality. Our work opens a plethora of research pathways
towards more explainable and transparent AI.
