Revising Image-Text Retrieval via Multi-Modal Entailment

• URL: http://arxiv.org/abs/2208.10126v1
• Date: Mon, 22 Aug 2022 07:58:54 GMT
• Title: Revising Image-Text Retrieval via Multi-Modal Entailment
• Authors: Xu Yan, Chunhui Ai, Ziqiang Cao, Min Cao, Sujian Li, Wenjie Chen, Guohong Fu
• Abstract summary: Many-to-many matching phenomenon is quite common in the widely-used image-text retrieval datasets. We propose a multi-modal entailment classifier to determine whether a sentence is entailed by an image plus its linked captions.
• Score: 25.988058843564335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An outstanding image-text retrieval model depends on high-quality labeled data. While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images. We observe that such a many-to-many matching phenomenon is quite common in the widely-used retrieval datasets, where one caption can describe up to 178 images. These large matching-lost data not only confuse the model in training but also weaken the evaluation accuracy. Inspired by visual and textual entailment tasks, we propose a multi-modal entailment classifier to determine whether a sentence is entailed by an image plus its linked captions. Subsequently, we revise the image-text retrieval datasets by adding these entailed captions as additional weak labels of an image and develop a universal variable learning rate strategy to teach a retrieval model to distinguish the entailed captions from other negative samples. In experiments, we manually annotate an entailment-corrected image-text retrieval dataset for evaluation. The results demonstrate that the proposed entailment classifier achieves about 78% accuracy and consistently improves the performance of image-text retrieval baselines.

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