用博弈论方法同时分析像素的独立与集体贡献，提升目标检测解释准确性。
Explaining Object Detectors via Collective Contribution of Pixels
- 基于谢帕利值与交互项的博弈论框架，捕捉像素集体影响。
- 在定位与分类任务中均优于现有方法，识别关键区域更准确。
- 适合需要可解释性且关注复杂视觉线索的研究者。
视觉解释对提升目标检测器的可靠性至关重要。目标检测器通过综合评估多个视觉特征来识别和定位目标实例。但在生成解释时，若忽略这些特征间的集体作用，可能导致遗漏组合线索或捕获虚假相关性。然而，现有方法通常仅关注单个像素的贡献，忽视了多像素的协同效应。为此，我们提出一种基于谢帕利值与交互项的博弈论方法，显式建模像素的个体与集体贡献。该方法可同时提供边界框定位与类别判断的解释，突出检测关键区域。大量实验表明，所提方法在识别重要区域方面优于当前最优方法。代码已公开于 https://github.com/tttt-0814/VX-CODE。
原文摘要 · Abstract (English)
Visual explanations for object detectors are crucial for enhancing their reliability. Object detectors identify and localize instances by assessing multiple visual features collectively. When generating explanations, overlooking these collective influences in detections may lead to missing compositional cues or capturing spurious correlations. However, existing methods typically focus solely on individual pixel contributions, neglecting the collective contribution of multiple pixels. To address this limitation, we propose a game-theoretic method based on Shapley values and interactions to explicitly capture both individual and collective pixel contributions. Our method provides explanations for both bounding box localization and class determination, highlighting regions crucial for detection. Extensive experiments demonstrate that the proposed method identifies important regions more accurately than state-of-the-art methods. The code is available at https://github.com/tttt-0814/VX-CODE
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