Language-Driven Region Pointer Advancement for Controllable Image Captioning

TL;DR

This paper introduces a novel region pointer advancement method for controllable image captioning, leveraging language structure to improve timing prediction and achieve state-of-the-art results.

Contribution

It proposes a language-structured approach for predicting region pointer timing, enhancing controllability and caption quality in image captioning models.

Findings

Timing prediction accuracy of 86.55% precision and 97.92% recall.

Improved captioning metrics over state-of-the-art methods.

Larger effective vocabulary size demonstrated.

Abstract

Controllable Image Captioning is a recent sub-field in the multi-modal task of Image Captioning wherein constraints are placed on which regions in an image should be described in the generated natural language caption. This puts a stronger focus on producing more detailed descriptions, and opens the door for more end-user control over results. A vital component of the Controllable Image Captioning architecture is the mechanism that decides the timing of attending to each region through the advancement of a region pointer. In this paper, we propose a novel method for predicting the timing of region pointer advancement by treating the advancement step as a natural part of the language structure via a NEXT-token, motivated by a strong correlation to the sentence structure in the training data. We find that our timing agrees with the ground-truth timing in the Flickr30k Entities test data with a precision of 86.55% and a recall of 97.92%. Our model implementing this technique improves the state-of-the-art on standard captioning metrics while additionally demonstrating a considerably larger effective vocabulary size.