arXiv:2606.26734v1 Announce Type: cross Abstract: The impact of real-world noise on Open Vocabulary Object Detectors (OV-ODs) remains poorly understood due to their architectural complexity. We present our comprehensive analysis Robust Onion, an empirical study that uses controlled synthetic visual degradations to peel OV-ODs layer-by-layer, revealing how, why, and where robustness degrades, systematically analyzing feature collapse. Our findings reveal that models with similar vision backbones exhibit comparable robustness, driven by similar feature collapse at similar layers, while factors s
The increasing complexity and deployment of advanced AI models like Open Vocabulary Object Detectors necessitate a deeper understanding of their real-world robustness.
Understanding the robustness and failure modes of OV-ODs is critical for reliable and safe deployment of AI systems in diverse, noisy environments.
This research provides a systematic framework for analyzing and improving the robustness of critical vision models, moving towards more reliable AI applications.
- · AI developers
- · Autonomous systems integrators
- · Defense contractors
- · Developers of brittle AI models
- · Organizations deploying unrobust AI
Improved performance and safety of AI-driven perception systems in real-world conditions.
Accelerated adoption of AI in applications requiring high reliability, such as robotics and surveillance.
Increased public and regulatory trust in AI systems due to better understanding and mitigation of failure points.
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Read at arXiv cs.AI