A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues

• URL: http://arxiv.org/abs/2305.04530v1
• Date: Mon, 8 May 2023 08:05:40 GMT
• Title: A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues
• Authors: Yunxin Li, Baotian Hu, Xinyu Chen, Yuxin Ding, Lin Ma, and Min Zhang
• Abstract summary: Conditional inference on joint textual and visual clues is a multi-modal reasoning task. We propose a Multi-modal Context Reasoning approach, named ModCR. We conduct extensive experiments on two corresponding data sets and experimental results show significantly improved performance.
• Score: 23.743431157431893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conditional inference on joint textual and visual clues is a multi-modal reasoning task that textual clues provide prior permutation or external knowledge, which are complementary with visual content and pivotal to deducing the correct option. Previous methods utilizing pretrained vision-language models (VLMs) have achieved impressive performances, yet they show a lack of multimodal context reasoning capability, especially for text-modal information. To address this issue, we propose a Multi-modal Context Reasoning approach, named ModCR. Compared to VLMs performing reasoning via cross modal semantic alignment, it regards the given textual abstract semantic and objective image information as the pre-context information and embeds them into the language model to perform context reasoning. Different from recent vision-aided language models used in natural language processing, ModCR incorporates the multi-view semantic alignment information between language and vision by introducing the learnable alignment prefix between image and text in the pretrained language model. This makes the language model well-suitable for such multi-modal reasoning scenario on joint textual and visual clues. We conduct extensive experiments on two corresponding data sets and experimental results show significantly improved performance (exact gain by 4.8% on PMR test set) compared to previous strong baselines. Code Link: \url{https://github.com/YunxinLi/Multimodal-Context-Reasoning}.

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