Papers with unimodal model

3 papers
Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts (2022.coling-1)

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Challenge: Recent multimodal information extraction approaches overestimate the significance of images.
Approach: They propose a general data splitting strategy to divide social media posts into two sets to achieve better performance under information extraction models of the corresponding modalities.
Outcome: The proposed method outperforms existing models on two different multimodal information extraction tasks.
MM-SHAP: A Performance-agnostic Metric for Measuring Multimodal Contributions in Vision and Language Models & Tasks (2023.acl-long)

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Challenge: Vision and language models exploit unrobust indicators in individual modalities instead of focusing on relevant information in each modality.
Approach: They propose a performance-agnostic multimodality score based on Shapley values that quantifies in which proportions a multimodal model uses individual modalities.
Outcome: The proposed model can quantify in which proportions a multimodal model uses individual modalities for different tasks and datasets.
How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input? (2022.coling-1)

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Challenge: Current language models have been criticised for learning language from text alone without connection between words and their meaning.
Approach: They propose to train models on more sources than text to provide the lacking connection between words and their meanings.
Outcome: The proposed model adaptation methods perform differently for different models and unimodal model counterparts perform on par with the VL models regardless of adaptation.

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