Semi Supervised Phrase Localization in a Bidirectional Caption-Image Retrieval Framework
This addresses the challenge of fine-grained image-text alignment for applications like retrieval and captioning, representing an incremental improvement over existing methods.
The authors tackled the problem of linking visual regions to textual phrases without explicit supervision for localization, achieving state-of-the-art results in semi-supervised phrase localization on MSCOCO and Flickr30K Entities datasets.
We introduce a novel deep neural network architecture that links visual regions to corresponding textual segments including phrases and words. To accomplish this task, our architecture makes use of the rich semantic information available in a joint embedding space of multi-modal data. From this joint embedding space, we extract the associative localization maps that develop naturally, without explicitly providing supervision during training for the localization task. The joint space is learned using a bidirectional ranking objective that is optimized using a $N$-Pair loss formulation. This training mechanism demonstrates the idea that localization information is learned inherently while optimizing a Bidirectional Retrieval objective. The model's retrieval and localization performance is evaluated on MSCOCO and Flickr30K Entities datasets. This architecture outperforms the state of the art results in the semi-supervised phrase localization setting.