Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering

• URL: http://arxiv.org/abs/2301.04366v1
• Date: Wed, 11 Jan 2023 09:16:34 GMT
• Title: Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering
• Authors: Paul Lerner, Olivier Ferret, Camille Guinaudeau
• Abstract summary: We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities. KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Our method is applicable to different neural network architectures and leads to a 9% relative-MRR and 15% relative-F1 gain for retrieval and reading comprehension.
• Score: 4.114444605090133
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction between the modalities is paramount to retrieve information and must be captured with complex fusion models. As these models require a lot of training data, we design this pre-training task from existing work in textual Question Answering. It consists in considering a sentence as a pseudo-question and its context as a pseudo-relevant passage and is extended by considering images near texts in multimodal documents. Our method is applicable to different neural network architectures and leads to a 9% relative-MRR and 15% relative-F1 gain for retrieval and reading comprehension, respectively, over a no-pre-training baseline.

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