Dependent Multi-Task Learning with Causal Intervention for Image Captioning

TL;DR

This paper introduces a causal intervention framework with multi-task learning for image captioning, addressing content inconsistency and informativeness issues by leveraging intermediate tasks and Pearl's do-calculus.

Contribution

It proposes a novel dependent multi-task learning framework with causal intervention (DMTCI) that improves caption quality by reducing spurious correlations and enhancing visual understanding.

Findings

Outperforms baseline models on standard benchmarks.

Achieves competitive results with state-of-the-art methods.

Effectively reduces content inconsistency in generated captions.

Abstract

Recent work for image captioning mainly followed an extract-then-generate paradigm, pre-extracting a sequence of object-based features and then formulating image captioning as a single sequence-to-sequence task. Although promising, we observed two problems in generated captions: 1) content inconsistency where models would generate contradicting facts; 2) not informative enough where models would miss parts of important information. From a causal perspective, the reason is that models have captured spurious statistical correlations between visual features and certain expressions (e.g., visual features of "long hair" and "woman"). In this paper, we propose a dependent multi-task learning framework with the causal intervention (DMTCI). Firstly, we involve an intermediate task, bag-of-categories generation, before the final task, image captioning. The intermediate task would help the model better understand the visual features and thus alleviate the content inconsistency problem. Secondly, we apply Pearl's do-calculus on the model, cutting off the link between the visual features and possible confounders and thus letting models focus on the causal visual features. Specifically, the high-frequency concept set is considered as the proxy confounders where the real confounders are inferred in the continuous space. Finally, we use a multi-agent reinforcement learning (MARL) strategy to enable end-to-end training and reduce the inter-task error accumulations. The extensive experiments show that our model outperforms the baseline models and achieves competitive performance with state-of-the-art models.