Thought-For-Food：推理链诱导的食物视觉问答
Thought-For-Food: Reasoning Chain Induced Food Visual Question Answering
AI总结：
针对现有VQA系统偏向西方食物的问题，本文提出在印度食物VQA中采用多步推理链，通过微调小模型并利用强化学习训练，使基线准确率平均提升10个百分点。
摘要：
印度饮食在文化和烹饪上的巨大多样性引起了人们对现有视觉问答（VQA）系统主要缺陷的关注，这些系统偏向于西方地区的食物。近期为印度食物构建VQA数据集的尝试是应对这一挑战的一步。然而，他们的VQA方法遵循两步过程：首先生成答案，随后对预期答案进行解释。在本研究中，我们主张食物VQA需要遵循多步推理过程才能得出准确答案，尤其是在印度食物的背景下，这涉及理解复杂的烹饪背景和识别各种食物之间的关系。基于这一假设，我们在问答（QA）上以最少的人工干预创建推理链。我们使用自动验证的推理链微调较小的LLM和VLM，并利用强化学习用更大数据进一步训练它们。通过推理链的增强，我们观察到基线准确率平均提高了10个百分点。我们提供了添加推理链对印度食物VQA任务影响的详细分析。索引术语 - FoodVQA、推理链、强化学习、知识图。
英文摘要：
The immense diversity in the culture and culinary of Indian cuisines calls attention to the major shortcoming of the existing Visual Question Answering(VQA) systems which are inclined towards the foods from Western region. Recent attempt towards building a VQA dataset for Indian food is a step towards addressing this challenge. However, their approach towards VQA follows a two-step process in which the answer is generated first, followed by the explanation of the expected answer. In this work, we claim that food VQA requires to follow a multi-step reasoning process to arrive at an accurate answer, especially in the context of India food, which involves understanding complex culinary context and identifying relationships between various food items. With this hypothesis we create reasoning chains upon the QA with minimal human intervention. We fine-tune smaller LLMs and VLMs with auto-validated reasoning chains and further train them using reinforcement learning with larger data. With augmentation of reasoning chains, we observed accuracy improvement of an average 10 percentage points on the baseline. We provide detailed analysis in terms the effect of addition of reasoning chains for the Indian Food VQA task. Index Terms - FoodVQA, Reasoning Chains, Reinforcement Learning, Knowledge Graph.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
