2026
Pathak, Priyank; Karuppasamy, M.; Baranwal, A.; Vyas, Shruti; Rawat, Yogesh Singh
Robust Onion: Peeling Open Vocab Object Detectors Under Noise Conference
European Conference on Computer Vision -- ECCV 2026, 2026.
Abstract | Tags: ECCV, Explainability, Interpretability | Links:
@conference{Pathak2026b,
title = {Robust Onion: Peeling Open Vocab Object Detectors Under Noise},
author = {Priyank Pathak and M. Karuppasamy and A. Baranwal and Shruti Vyas and Yogesh Singh Rawat},
url = {https://arxiv.org/abs/2606.26734},
year = {2026},
date = {2026-09-07},
urldate = {2026-09-07},
publisher = {European Conference on Computer Vision -- ECCV 2026},
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 such as pretraining strategy, architectural nuances, and caption supervision contribute little. Robustness is primarily governed by the image domain rather than annotations, explaining the similar robustness impact on COCO and LVIS, and why datasets like ODinW-13 can give an impression of inflated robustness due to large, isolated objects. Finally, we validate our insights by improving robustness on real-world BDD100K, WiderFace, and VisDRONE via our lightweight plug-and-play NN & TK0 approach, using 96x fewer trainable parameters than end-to-end training. We also explain the prior works' robustness observations.},
keywords = {ECCV, Explainability, Interpretability},
pubstate = {published},
tppubtype = {conference}
}
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 such as pretraining strategy, architectural nuances, and caption supervision contribute little. Robustness is primarily governed by the image domain rather than annotations, explaining the similar robustness impact on COCO and LVIS, and why datasets like ODinW-13 can give an impression of inflated robustness due to large, isolated objects. Finally, we validate our insights by improving robustness on real-world BDD100K, WiderFace, and VisDRONE via our lightweight plug-and-play NN & TK0 approach, using 96x fewer trainable parameters than end-to-end training. We also explain the prior works' robustness observations.