Checkmate：可解释且可说明的遥感视觉问答是终局
Checkmate: interpretable and explainable RSVQA is the endgame
AI总结：
本文提出均衡偏差的RSVQA数据集Chessboard和可解释模型Checkmate，通过定位关键图像单元格提升遥感视觉问答的透明度与可信度。
摘要：
遥感视觉问答（RSVQA）在确保模型决策既可理解又植根于视觉内容方面面临独特挑战。当前模型往往缺乏可解释性与可说明性，并且数据集分布中的偏差会导致捷径学习。在本工作中，我们通过引入一个新颖的RSVQA数据集Chessboard来解决这些问题，该数据集通过3,123,253个问题和均衡的答案分布来最小化偏差。每个答案都与图像中的一个或多个单元格相关联，从而支持细粒度的视觉推理。在此数据集的基础上，我们开发了一个可说明且可解释的模型Checkmate，该模型能够识别与其决策最相关的图像单元格。通过在多种模型架构上进行广泛实验，我们表明我们的方法提高了透明度，并支持RSVQA系统中更值得信赖的决策。
英文摘要：
Remote Sensing Visual Question Answering (RSVQA) presents unique challenges in ensuring that model decisions are both understandable and grounded in visual content. Current models often suffer from a lack of interpretability and explainability, as well as from biases in dataset distributions that lead to shortcut learning. In this work, we tackle these issues by introducing a novel RSVQA dataset, Chessboard, designed to minimize biases through 3'123'253 questions and a balanced answer distribution. Each answer is linked to one or more cells within the image, enabling fine-grained visual reasoning. Building on this dataset, we develop an explainable and interpretable model called Checkmate that identifies the image cells most relevant to its decisions. Through extensive experiments across multiple model architectures, we show that our approach improves transparency and supports more trustworthy decision-making in RSVQA systems.
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。
