Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements

arXiv:2404.08526v1 Announce Type: new
Abstract: To make sense of their surroundings, intelligent systems must transform complex sensory inputs to structured codes that are reduced to task-relevant information such as object category. Biological agents achieve this in a largely autonomous manner, presumably via self-\allowbreak super-\allowbreak vised learning. Whereas previous attempts to model the underlying mechanisms were largely discriminative in nature, there is ample evidence that the brain employs a generative model of the world. Here, we propose that eye movements, in …

arxiv cs.cv framework image modeling movements self-supervised learning supervised learning type