  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 in
distributions. Recent works have explored leveraging large language models and
diffusion models to generate changes in the background. However, these methods
either lack in offering control over the changes to be made or distort the
object semantics, making them unsuitable for the task. Our method, on the other
hand, can induce diverse object-to-background changes while preserving the
original semantics and appearance of the object. To achieve this goal, we
harness the generative capabilities of text-to-image, image-to-text, and
image-to-segment models to automatically generate a broad spectrum of
object-to-background changes. We induce both natural and adversarial background
changes by either modifying the textual prompts or optimizing the latents and
textual embedding of text-to-image models. We produce various versions of
standard vision datasets (ImageNet, COCO), incorporating either diverse and
realistic backgrounds into the images or introducing color, texture, and
adversarial changes in the background. We conduct extensive experiment to
analyze the robustness of vision-based models against object-to-background
context variations across diverse tasks. Code
https://github.com/Muhammad-Huzaifaa/ObjectCompose.git
