From "Umwelt" to "World" models

Learning objects without supervision remains difficult outside restricted settings despite their central role in human vision. This talk argues that assumptions built into many object-centred models conflict with real-world data, making the usual problem formulation inadequate at scale. It examines that mismatch and presents ongoing work intended to support more meaningful learned representations.