学习观察：视觉-语言模型的认知注意力对齐
Learning to Look: Cognitive Attention Alignment with Vision-Language Models
AI总结：
提出利用视觉-语言模型自动生成语义注意力图，通过辅助损失对齐CNN注意力，无需手动标注，在ColoredMNIST和DecoyMNIST上实现先进性能，减少捷径依赖并提升泛化。
摘要：
卷积神经网络（CNNs）经常通过利用表面相关性“作弊”，这引发了对其是否基于正确理由进行预测的担忧。受认知科学启发，该科学强调注意力在人类稳健感知中的作用，近期方法试图通过基于概念的监督和解释正则化来引导模型注意力。然而，这些技术依赖于劳动密集型的专家提供的标注，限制了其可扩展性。我们提出一个可扩展框架，利用视觉-语言模型通过自然语言提示自动生成语义注意力图。通过引入一个辅助损失，将CNN注意力与这些语言引导的图对齐，我们的方法在无需手动标注的情况下促进了更可靠和认知上合理的决策。在具有挑战性的数据集ColoredMNIST和DecoyMNIST上的实验表明，我们的方法在ColorMNIST上达到了最先进的性能，并在DecoyMNIST上与依赖标注的基线保持竞争力，展示了改进的泛化能力、减少的捷径依赖以及更好地反映人类直觉的模型注意力。
英文摘要：
Convolutional Neural Networks (CNNs) frequently "cheat" by exploiting superficial correlations, raising concerns about whether they make predictions for the right reasons. Inspired by cognitive science, which highlights the role of attention in robust human perception, recent methods have sought to guide model attention using concept-based supervision and explanation regularization. However, these techniques depend on labor-intensive, expert-provided annotations, limiting their scalability. We propose a scalable framework that leverages vision-language models to automatically generate semantic attention maps using natural language prompts. By introducing an auxiliary loss that aligns CNN attention with these language-guided maps, our approach promotes more reliable and cognitively plausible decision-making without manual annotation. Experiments on challenging datasets, ColoredMNIST and DecoyMNIST, show that our method achieves state-of-the-art performance on ColorMNIST and remains competitive with annotation-heavy baselines on DecoyMNIST, demonstrating improved generalization, reduced shortcut reliance, and model attention that better reflects human intuition.
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。
