Leveraging Image Augmentation for Object Manipulation: Towards Interpretable Controllability in Object-Centric Learning

arXiv:2310.08929v3 Announce Type: replace
Abstract: The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of symbol-like entities. Especially in the field of computer vision, object-centric learning (OCL) is extensively researched to better understand complex scenes by acquiring object representations or slots. While recent studies in OCL have made strides with complex images or videos, the interpretability and interactivity over object representation remain largely …

abstract artificial artificial neural networks arxiv augmentation computer computer vision cs.cv human image manipulation networks neural networks recognition skills terms through type vision world