Defoiling Foiled Image Captions

TL;DR

This paper introduces a method for detecting deliberately altered image captions by leveraging explicit object information, achieving state-of-the-art results and revealing dataset biases.

Contribution

The paper presents a simple, interpretable model that effectively detects caption foiling using object-based representations, outperforming previous methods.

Findings

Models surpass human performance on the dataset.

Explicit object information improves detection accuracy.

Dataset exhibits strong linguistic biases.

Abstract

We address the task of detecting foiled image captions, i.e. identifying whether a caption contains a word that has been deliberately replaced by a semantically similar word, thus rendering it inaccurate with respect to the image being described. Solving this problem should in principle require a fine-grained understanding of images to detect linguistically valid perturbations in captions. In such contexts, encoding sufficiently descriptive image information becomes a key challenge. In this paper, we demonstrate that it is possible to solve this task using simple, interpretable yet powerful representations based on explicit object information. Our models achieve state-of-the-art performance on a standard dataset, with scores exceeding those achieved by humans on the task. We also measure the upper-bound performance of our models using gold standard annotations. Our analysis reveals that the simpler model performs well even without image information, suggesting that the dataset contains strong linguistic bias.