  The challenge of Out-Of-Distribution (OOD) robustness remains a critical
hurdle towards deploying deep vision models. Vision-Language Models (VLMs) have
recently achieved groundbreaking results. VLM-based open-vocabulary object
detection extends the capabilities of traditional object detection frameworks,
enabling the recognition and classification of objects beyond predefined
categories. Investigating OOD robustness in recent open-vocabulary object
detection is essential to increase the trustworthiness of these models. This
study presents a comprehensive robustness evaluation of the zero-shot
capabilities of three recent open-vocabulary (OV) foundation object detection
models: OWL-ViT, YOLO World, and Grounding DINO. Experiments carried out on the
robustness benchmarks COCO-O, COCO-DC, and COCO-C encompassing distribution
shifts due to information loss, corruption, adversarial attacks, and
geometrical deformation, highlighting the challenges of the model's robustness
to foster the research for achieving robustness. Source code shall be made
available to the research community on GitHub.
