CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering

• URL: http://arxiv.org/abs/2501.01371v1
• Date: Thu, 02 Jan 2025 17:30:53 GMT
• Title: CLIP-UP: CLIP-Based Unanswerable Problem Detection for Visual Question Answering
• Authors: Ben Vardi, Oron Nir, Ariel Shamir,
• Abstract summary: We propose CLIP-UP: CLIP-based Unanswerable Problem detection. It is a novel lightweight method for equipping Vision-Language Models with the ability to withhold answers to unanswerable questions. It achieves state-of-the-art results on the MM-UPD benchmark for assessing unanswerability in multiple-choice Visual Question Answering.
• Score: 14.079577086689659
• License:
• Abstract: Recent Vision-Language Models (VLMs) have demonstrated remarkable capabilities in visual understanding and reasoning, and in particular on multiple-choice Visual Question Answering (VQA). Still, these models can make distinctly unnatural errors, for example, providing (wrong) answers to unanswerable VQA questions, such as questions asking about objects that do not appear in the image. To address this issue, we propose CLIP-UP: CLIP-based Unanswerable Problem detection, a novel lightweight method for equipping VLMs with the ability to withhold answers to unanswerable questions. By leveraging CLIP to extract question-image alignment information, CLIP-UP requires only efficient training of a few additional layers, while keeping the original VLMs' weights unchanged. Tested across LLaVA models, CLIP-UP achieves state-of-the-art results on the MM-UPD benchmark for assessing unanswerability in multiple-choice VQA, while preserving the original performance on other tasks.

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