FACTOR: Counterfactual Training-Free Test-Time Adaptation for Open-Vocabulary Object Detection
Open-vocabulary object detection often fails under distribution shifts, as it can be misled by spurious correlations between non-causal visual attributes (e.g., brightness, texture) and object categories. Existing test-time adaptation (TTA) methods either depend on costly online optimization or perform global calibration, overlooking the attribute-specific nature of these failures. To address this, we propose FACTOR (counterFACtual training-free Test-time adaptation for Open-vocabulaRy object detection), a lightweight framework grounded in counterfactual reasoning. By perturbing test images along non-causal attributes and comparing region-level predictions between original and counterfact...
| Authors | Kaixiang Zhao, Mao Ye, Lihua Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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