Cross-modal Retrieval for Knowledge-based Visual Question Answering

• URL: http://arxiv.org/abs/2401.05736v1
• Date: Thu, 11 Jan 2024 08:40:16 GMT
• Title: Cross-modal Retrieval for Knowledge-based Visual Question Answering
• Authors: Paul Lerner, Olivier Ferret (LIST (CEA), DIASI), Camille Guinaudeau
• Abstract summary: Cross-modal retrieval may help bridge the semantic gap between an entity and its depictions. We provide empirical evidence through experiments with a multimodal dual encoder, namely CLIP, on the recent ViQuAE, InfoSeek, and Encyclopedic-VQA datasets.
• Score: 0.5852077003870417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge-based Visual Question Answering about Named Entities is a challenging task that requires retrieving information from a multimodal Knowledge Base. Named entities have diverse visual representations and are therefore difficult to recognize. We argue that cross-modal retrieval may help bridge the semantic gap between an entity and its depictions, and is foremost complementary with mono-modal retrieval. We provide empirical evidence through experiments with a multimodal dual encoder, namely CLIP, on the recent ViQuAE, InfoSeek, and Encyclopedic-VQA datasets. Additionally, we study three different strategies to fine-tune such a model: mono-modal, cross-modal, or joint training. Our method, which combines mono-and cross-modal retrieval, is competitive with billion-parameter models on the three datasets, while being conceptually simpler and computationally cheaper.

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