Reward-Guided Semantic Evolution for Test-time Adaptive Object Detection
Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribution shifts, primarily due to semantic misalignment between text embeddings and shifted visual embeddings of region proposals. While recent test-time adaptive object detection methods for VLM-based either rely on costly backpropagation or bypass semantic misalignment via external memory, none directly and efficiently align text and vision in a training-free manner. To address this, we propose Reward-Guided Semantic Evolution (RGSE), a training-free framework that directly refines the text embeddings at test time. Inspired by evolutionary s...
| Authors | Lihua Zhou, Mao Ye, Xiatian Zhu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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