Hard-Negatives or Non-Negatives? A Hard-Negative Selection Strategy for Cross-Modal Retrieval Using the Improved Marginal Ranking Loss

Cross-modal learning has gained a lot of interest recently, and many applications of it, such as image-text retrieval, cross-modal video search, or video captioning have been proposed. In this work, we deal with the cross-modal video retrieval problem. The state-of-the-art approaches are based on deep network architectures, and rely on mining hard-negative samples during training to optimize the selection of the network’s parameters. Starting from a state-of-the-art cross-modal architecture that uses the improved marginal ranking loss function, we propose a simple strategy for hard-negative mining to identify which training samples are hard-negatives and which, although presently treated as hard-negatives, are likely not negative samples at all and shouldn’t be treated as such. Additionally, to take full advantage of network models trained using different de-sign choices for hard-negative mining, we examine model combination strategies, and we design a hybrid one effectively combining large numbers of trained models.