FiVL: A Framework for Improved Vision-Language Alignment

• URL: http://arxiv.org/abs/2412.14672v1
• Date: Thu, 19 Dec 2024 09:24:10 GMT
• Title: FiVL: A Framework for Improved Vision-Language Alignment
• Authors: Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le, Man Luo, Shachar Rosenman, Sayak Paul, Shao-Yen Tseng, Vasudev Lal,
• Abstract summary: We introduce FiVL, a novel method for constructing datasets designed to train LVLMs for enhanced visual grounding.<n>These datasets can be utilized for both training and assessing an LVLM's ability to use image content as substantive evidence.<n>To demonstrate the utility of our dataset, we introduce an innovative training task that outperforms baselines alongside a validation method and application for explainability.
• Score: 10.184567639685321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensuring these models utilize visual information as effectively as linguistic content when both modalities are necessary to formulate an accurate answer. We hypothesize that hallucinations arise due to the lack of effective visual grounding in current LVLMs. This issue extends to vision-language benchmarks, where it is difficult to make the image indispensable for accurate answer generation, particularly in vision question-answering tasks. In this work, we introduce FiVL, a novel method for constructing datasets designed to train LVLMs for enhanced visual grounding and to evaluate their effectiveness in achieving it. These datasets can be utilized for both training and assessing an LVLM's ability to use image content as substantive evidence rather than relying solely on linguistic priors, providing insights into the model's reliance on visual information. To demonstrate the utility of our dataset, we introduce an innovative training task that outperforms baselines alongside a validation method and application for explainability. The code is available at https://github.com/IntelLabs/fivl.

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