Defoiling Foiled Image Captions
This addresses a fine-grained image understanding task for computer vision and NLP, but it is incremental as it builds on existing datasets and methods.
The paper tackles the problem of detecting foiled image captions, where words are replaced to make captions inaccurate, and demonstrates that using simple object-based representations achieves state-of-the-art performance, exceeding human scores on a standard dataset.
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.