  Existing image-text matching approaches typically leverage triplet loss with
online hard negatives to train the model. For each image or text anchor in a
training mini-batch, the model is trained to distinguish between a positive and
the most confusing negative of the anchor mined from the mini-batch (i.e.
online hard negative). This strategy improves the model's capacity to discover
fine-grained correspondences and non-correspondences between image and text
inputs. However, the above approach has the following drawbacks: (1) the
negative selection strategy still provides limited chances for the model to
learn from very hard-to-distinguish cases. (2) The trained model has weak
generalization capability from the training set to the testing set. (3) The
penalty lacks hierarchy and adaptiveness for hard negatives with different
"hardness" degrees. In this paper, we propose solutions by sampling negatives
offline from the whole training set. It provides "harder" offline negatives
than online hard negatives for the model to distinguish. Based on the offline
hard negatives, a quintuplet loss is proposed to improve the model's
generalization capability to distinguish positives and negatives. In addition,
a novel loss function that combines the knowledge of positives, offline hard
negatives and online hard negatives is created. It leverages offline hard
negatives as the intermediary to adaptively penalize them based on their
distance relations to the anchor. We evaluate the proposed training approach on
three state-of-the-art image-text models on the MS-COCO and Flickr30K datasets.
Significant performance improvements are observed for all the models, proving
the effectiveness and generality of our approach. Code is available at
https://github.com/sunnychencool/AOQ
