Explicit Semantic–Spatial Alignment for Open-Vocabulary Object Detection

Open-Vocabulary object Detection (OVD) typically relies onVision–Language Models (VLMs) to associate visual regions with arbi-trary textual concepts. To compensate for the limited localization capa-bility of VLMs, existing approaches often incorporate large pre-trainedvisual features that inherently encode object-centric spatial cues. How-ever, these features are commonly introduced through direct fusion orshallow adaptation, which fails to explicitly align semantic representa-tions with object-level spatial information, resulting in imprecise local-ization. In this paper, we propose a novel framework for explicit seman-tic–spatial alignment in open-vocabulary object detection. Rather thandirectly merging pre-trained object-centric visual features, our methodprogressively aligns semantic representations with object-level spatialcues through a staged alignment process. Specifically, we introduce alightweight spatial adapter that spatially recalibrates auxiliary object-centric visual features to suppress background responses and emphasizesalient object regions. Building upon this, we design a frequency-awarefusion mechanism that decomposes the adapted object-centric visual fea-tures into spectral components and adaptively injects object-sensitivespatial cues into semantic features at multiple levels. This targeted fu-sion strategy enables effective semantic alignment while preserving theoriginal semantic embedding space. Extensive experiments on the OV-COCO and OV-LVIS benchmarks demonstrate the effectiveness of ourapproach against state-of-the-art methods.