Asking More Informative Questions for Grounded Retrieval
Sedrick Keh, Justin T. Chiu, Daniel Fried

TL;DR
This paper introduces a method for grounded image identification that asks more informative, open-ended questions, effectively handling presupposition errors in VQA models, leading to improved accuracy and efficiency.
Contribution
It proposes a novel approach to formulate open-ended questions and incorporate presupposition handling into question selection and belief updates in grounded retrieval tasks.
Findings
Increased accuracy by 14% over previous state-of-the-art.
Achieved 48% more efficient games in human evaluations.
Effectively manages presupposition errors in VQA models.
Abstract
When a model is trying to gather information in an interactive setting, it benefits from asking informative questions. However, in the case of a grounded multi-turn image identification task, previous studies have been constrained to polar yes/no questions, limiting how much information the model can gain in a single turn. We present an approach that formulates more informative, open-ended questions. In doing so, we discover that off-the-shelf visual question answering (VQA) models often make presupposition errors, which standard information gain question selection methods fail to account for. To address this issue, we propose a method that can incorporate presupposition handling into both question selection and belief updates. Specifically, we use a two-stage process, where the model first filters out images which are irrelevant to a given question, then updates its beliefs about which…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning · Advanced Image and Video Retrieval Techniques
