Enhancing Image-Text Retrieval via Region-Grid Interaction and Semantic Calibration

Image-Text retrieval requires accurate semantic alignment between visual content and natural language descriptions. Most existing methods primarily rely on region features, which are typically object-centric and may overlook important contextual information. In contrast, grid features provide denser spatial coverage and richer local details, but often lack explicit semantic structure. To better exploit their complementarity, we propose a novel Region-Grid Interaction and Calibration Network (RGICN) for Image-Text retrieval. Specifically, we first design a Global-Guided Feature Interaction Module to promote information exchange between region and grid features under the guidance of global visual semantics, allowing object-level semantics and contextual cues to complement each other. We then introduce a Text-Guided Feature Calibration Module, which leverages auxiliary image descriptions to calibrate visual features by suppressing redundant and text-irrelevant content. Finally, an Adaptive Gating Fusion Module is developed to dynamically integrate multiple visual representations according to the input image, yielding a more comprehensive and discriminative visual embedding. Extensive experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of RGICN and its competitive performance against recent state-of-the-art methods.