Distribution-to-Points Matching for Image Text Retrieval

Eliminating semantic discrepancy between different modalities is the ultimate goal of image text retrieval. However, most of the existing methods only focus on retrieval of the ground-truth instance while ignoring those semantically similar instances yet unlabeled as positives, which causes the phenomenon of one-to-many correspondence. The mainstream solutions of this research are mainly based on uncertainty learning and the exploration of one-to-many correspondence is still insufficient albeit their significant progress. Therefore, this work develops a novel Distribution-to-Points (termed D2P) matching mechanism for image-text retrieval to capture the one-to-many correspondence between multiple samples and a given query via hypergraph modeling. Specifically, a given query is first mapped as a probabilistic embedding to learn its true semantic distribution based on Mahalanobis distance. Then each candidate instance in a mini-batch is regarded as a hypergraph node with its mean semantics while a Gaussian query is modeled as a hyperedge to capture the semantic correlations beyond the pair between candidate points and the query. Moreover, an energy-based semantic modeling framework is developed to pull all similar candidates (not only the ground truth) close to their query while pushing those dissimilar ones far away. In the end, distribution-to-points matching is learned based on the similarity measurement over the Mahalanobis distance, which considers semantic variance to perform many-to-one correspondence well. Experimental results on several widely used datasets and under various evaluation metrics confirm our superiority and effectiveness in improving the retrieval ability of the baseline including ground-truth matching and semantic multiplicity for image text retrieval.