Contextual information plays a critical role in object recognition models within computer vision, where changes in context can significantly affect accuracy, underscoring models' dependence on contextual cues. This study investigates how context manipulation influences both model accuracy and feature attribution, providing insights into the reliance of object recognition models on contextual information as understood through the lens of feature attribution methods. We employ a range of feature attribution techniques to decipher the reliance of deep neural networks on context in object recognition tasks. Using the ImageNet-9 and our curated ImageNet-CS datasets, we conduct experiments to evaluate the impact of contextual variations, analyzed through feature attribution methods. Our findings reveal several key insights: (a) Correctly classified images predominantly emphasize object volume attribution over context volume attribution. (b) The dependence on context remains relatively stable across different context modifications, irrespective of classification accuracy. (c) Context change exerts a more pronounced effect on model performance than Context perturbations. (d) Surprisingly, context attribution in `no-information' scenarios is non-trivial. Our research moves beyond traditional methods by assessing the implications of broad-level modifications on object recognition, either in the object or its context.

翻译：在计算机视觉领域，上下文信息在目标识别模型中扮演着关键角色，其中上下文的变化会显著影响模型准确率，这突显了模型对上下文线索的依赖性。本研究探讨了上下文操控如何同时影响模型准确率和特征归因，从而通过特征归因方法的视角，深入理解目标识别模型对上下文信息的依赖机制。我们采用一系列特征归因技术来解读深度神经网络在目标识别任务中对上下文的依赖程度。利用ImageNet-9数据集及我们自建的ImageNet-CS数据集，我们进行了实验以评估上下文变化的影响，并通过特征归因方法进行分析。我们的研究结果揭示了若干关键发现：(a) 正确分类的图像主要强调目标区域而非上下文区域的特征归因。(b) 在不同上下文修改下，模型对上下文的依赖保持相对稳定，与分类准确率无关。(c) 上下文变化对模型性能的影响比上下文扰动更为显著。(d) 令人惊讶的是，在“无信息”场景中，上下文归因并非微不足道。我们的研究超越了传统方法，通过评估目标或其上下文在宏观层面上的修改对目标识别的影响，提供了新的见解。