Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts

• URL: http://arxiv.org/abs/2405.14874v4
• Date: Fri, 6 Sep 2024 15:11:19 GMT
• Title: Open-Vocabulary Object Detectors: Robustness Challenges under Distribution Shifts
• Authors: Prakash Chandra Chhipa, Kanjar De, Meenakshi Subhash Chippa, Rajkumar Saini, Marcus Liwicki,
• Abstract summary: Vision-Language Models (VLMs) have recently achieved groundbreaking results. Investigating OOD robustness in VLM 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.
• Score: 6.486569431242123
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Project page: https://prakashchhipa.github.io/projects/ovod_robustness

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