AI RESEARCH
DetRefiner: Model-Agnostic Detection Refinement with Feature Fusion Transformer
arXiv CS.CV
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ArXi:2605.10190v1 Announce Type: new Open-vocabulary object detection (OVOD) aims to detect both seen and unseen categories, yet existing methods often struggle to generalize to novel objects due to limited integration of global and local contextual cues. We propose DetRefiner, a simple yet effective plug-and-play framework that learns to fuse global and local features to refine open-vocabulary detection. DetRefiner processes global image features and patch-level image features from foundational models (e.g., DINOv3) through a lightweight Transformer encoder.