Learning to Embed Semantic Similarity for Joint Image-Text Retrieval
Noam Malali, Yosi Keller · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021
We present a deep learning approach for learning the joint semantic embeddings of images and captions in a euclidean space, such that the semantic similarity is approximated by the$L_{2}$distances in the embedding space. For that, we introduce a metric learning scheme that utilizes multitask learning to learn the embedding ofidenticalsemantic concepts using a center loss. By introducing a differentiable quantization scheme into the end-to-end trainable network, we derive a semantic embedding of semanticallysimilarconcepts in euclidean space. We also propose a novel metric learning formulation using an adaptive margin hinge loss, that is refined during the training phase. The proposed scheme was applied to the MS-COCO, Flicke30K and Flickr8K datasets, and was shown to compare favorably with contemporary state-of-the-art approaches.