VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasing
<|reference_start|>VEglue: Testing Visual Entailment Systems via Object-Aligned Joint Erasing: Visual entailment (VE) is a multimodal reasoning task consisting of image-sentence pairs whereby a promise is defined by an image, and a hypothesis is described by a sentence. The goal is to predict whether the image semantically entails the sentence. VE systems have been widely adopted in many downstream tasks. Metamorphic testing is the commonest technique for AI algorithms, but it poses a significant challenge for VE testing. They either only consider perturbations on single modality which would result in ineffective tests due to the destruction of the relationship of image-text pair, or just conduct shallow perturbations on the inputs which can hardly detect the decision error made by VE systems. Motivated by the fact that objects in the image are the fundamental element for reasoning, we propose VEglue, an object-aligned joint erasing approach for VE systems testing. It first aligns the object regions in the premise and object descriptions in the hypothesis to identify linked and un-linked objects. Then, based on the alignment information, three Metamorphic Relations are designed to jointly erase the objects of the two modalities. We evaluate VEglue on four widely-used VE systems involving two public datasets. Results show that VEglue could detect 11,609 issues on average, which is 194%-2,846% more than the baselines. In addition, VEglue could reach 52.5% Issue Finding Rate (IFR) on average, and significantly outperform the baselines by 17.1%-38.2%. Furthermore, we leverage the tests generated by VEglue to retrain the VE systems, which largely improves model performance (50.8% increase in accuracy) on newly generated tests without sacrificing the accuracy on the original test set.<|reference_end|>
@article{chang2024veglue:,
    title={VEglue: Testing Visual Entailment Systems via Object-Aligned Joint
  Erasing},
    author={Zhiyuan Chang, Mingyang Li, Junjie Wang, Cheng Li, Qing Wang},
    journal={arXiv preprint arXiv:2403.02581},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.02581},
    primaryClass={cs.CV cs.SE}
}