CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation

TL;DR

This paper introduces CIC-BART-SSA, a novel controllable image captioning model that uses structured semantic augmentation with AMR to improve diversity, focus, and controllability of generated captions.

Contribution

The paper proposes a new SSA framework using AMR for dataset augmentation and a tailored CIC-BART-SSA model that enhances controllability and diversity in image captioning.

Findings

CIC-BART-SSA outperforms state-of-the-art models in diversity and text quality.

The SSA framework increases dataset semantic and spatial diversity.

The model effectively generalizes to highly focused captioning scenarios.

Abstract

Controllable Image Captioning (CIC) aims at generating natural language descriptions for an image, conditioned on information provided by end users, e.g., regions, entities or events of interest. However, available image-language datasets mainly contain captions that describe the entirety of an image, making them ineffective for training CIC models that can potentially attend to any subset of regions or relationships. To tackle this challenge, we propose a novel, fully automatic method to sample additional focused and visually grounded captions using a unified structured semantic representation built on top of the existing set of captions associated with an image. We leverage Abstract Meaning Representation (AMR), a cross-lingual graph-based semantic formalism, to encode all possible spatio-semantic relations between entities, beyond the typical spatial-relations-only focus of current methods. We use this Structured Semantic Augmentation (SSA) framework to augment existing image-caption datasets with the grounded controlled captions, increasing their spatial and semantic diversity and focal coverage. We then develop a new model, CIC-BART-SSA, specifically tailored for the CIC task, that sources its control signals from SSA-diversified datasets. We empirically show that, compared to SOTA CIC models, CIC-BART-SSA generates captions that are superior in diversity and text quality, are competitive in controllability, and, importantly, minimize the gap between broad and highly focused controlled captioning performance by efficiently generalizing to the challenging highly focused scenarios. Code is available at https://github.com/SamsungLabs/CIC-BART-SSA.