ObjectCompose: Evaluating Resilience of Vision-Based Models on Object-to-Background Compositional Changes

arXiv:2403.04701v1 Announce Type: new
Abstract: Given the large-scale multi-modal training of recent vision-based models and their generalization capabilities, understanding the extent of their robustness is critical for their real-world deployment. In this work, we evaluate the resilience of current vision-based models against diverse object-to-background context variations. The majority of robustness evaluation methods have introduced synthetic datasets to induce changes to object characteristics (viewpoints, scale, color) or utilized image transformation techniques (adversarial changes, common corruptions) on real images to simulate shifts …

abstract arxiv capabilities context cs.ai cs.cv current deployment diverse modal multi-modal object resilience robustness scale training type understanding vision work world