Sequential Latent Spaces for Modeling the Intention During Diverse Image Captioning

TL;DR

This paper introduces Seq-CVAE, a novel model that learns a distinct latent space for each word position in image captioning, enabling better control over diversity and maintaining sentence quality.

Contribution

Seq-CVAE is the first to learn a sequential latent space for each word, capturing intention and improving diversity in image captioning.

Findings

Significantly improves diversity metrics on MSCOCO

Performs on par with baselines in sentence quality

Demonstrates effective anticipation of sentence continuation

Abstract

Diverse and accurate vision+language modeling is an important goal to retain creative freedom and maintain user engagement. However, adequately capturing the intricacies of diversity in language models is challenging. Recent works commonly resort to latent variable models augmented with more or less supervision from object detectors or part-of-speech tags. Common to all those methods is the fact that the latent variable either only initializes the sentence generation process or is identical across the steps of generation. Both methods offer no fine-grained control. To address this concern, we propose Seq-CVAE which learns a latent space for every word position. We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future. We illustrate the efficacy of the proposed approach to anticipate the sentence continuation on the challenging MSCOCO dataset, significantly improving diversity metrics compared to baselines while performing on par w.r.t sentence quality.