ROOT: Region-Word Alignment With Partial Optimal Transport for Open-Vocabulary Object Detection.

Open-vocabulary object detection (OVD) aims to detect novel object concepts by mining region-word correspondences from image-text pairs, yet current methods often produce false correspondences. While some strategies (e.g., one-to-one matching) were proposed to mitigate this issue, they often sacrifice numerous valuable region-word pairs during the matching process. To overcome these challenges, we propose a novel comprehensive alignment method, named Region-word Alignment with Partial Optimal Transport (ROOT) framework, which reframes the region-word matching task as a problem of partial distribution alignment. Unlike traditional optimal transport, which shifts the full mass of the distribution, partial optimal transport enables selective matching, making it more robust to noise in region and word alignment. Specifically, ROOT first employs partial optimal transport to obtain an optimal transport plan for region and word feature alignment. This transport plan is then used to compute a matching reliability score for each region-word pair, which reweights the contrastive alignment loss to enhance accuracy. By enabling more flexible and reliable region-text matches, ROOT significantly reduces misalignment errors while preserving valuable region-word correspondences. Extensive experiments on standard benchmarks OV-COCO and OV-LVIS show that our ROOT outperforms the previous state-of-the-art works, demonstrating the effectiveness of our approach.