[Submitted on 1 Apr 2024]
View a PDF of the paper titled Investigating Robustness of Open-Vocabulary Foundation Object Detectors under Distribution Shifts, by Prakash Chandra Chhipa and 4 other authors
Abstract:The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Open-vocabulary object detection extends the capabilities of traditional object detection frameworks to recognize and classify objects beyond predefined categories. Investigating OOD robustness in open-vocabulary object detection is essential to increase the trustworthiness of these models. This study presents a comprehensive robustness comparison of zero-shot capabilities of three recent open-vocabulary foundation object detection models, namely OWL-ViT, YOLO World, and Grounding DINO. Experiments carried out on the COCO-O and COCO-C benchmarks encompassing distribution shifts highlight the challenges of the models’ robustness. Source code shall be made available to the research community on GitHub.
Submission history
From: Prakash Chandra Chhipa [view email]
[v1]
Mon, 1 Apr 2024 14:18:15 UTC (3,296 KB)
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