不要根据上下文判断对象：学习克服上下文偏见
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
摘要：
现有的模型通常利用对象及其上下文之间的共现来提高识别精度。然而，强烈依赖于上下文风险模型的泛化，特别是当典型的同现模式是缺席。这项工作的重点是解决这种背景偏见，以提高学习的特征表示的鲁棒性。我们的目标是在没有上下文的情况下准确识别一个类别，而不会在与上下文同时出现时影响性能。我们的核心思想是去相关的特征表示的一个类别，从其共同出现的背景。我们通过学习一个特征子空间来实现这一点，该特征子空间明确表示在没有上下文的情况下发生的类别，同时表示类别和上下文的联合特征子空间。我们非常简单而有效的方法可扩展到两个多标签任务-对象和属性分类。在4个具有挑战性的数据集上，我们证明了我们的方法在减少上下文偏差方面的有效性。
英文摘要：
Existing models often leverage co-occurrences between objects and their context to improve recognition accuracy. However, strongly relying on context risks a model's generalizability, especially when typical co-occurrence patterns are absent. This work focuses on addressing such contextual biases to improve the robustness of the learnt feature representations. Our goal is to accurately recognize a category in the absence of its context, without compromising on performance when it co-occurs with context. Our key idea is to decorrelate feature representations of a category from its co-occurring context. We achieve this by learning a feature subspace that explicitly represents categories occurring in the absence of context along side a joint feature subspace that represents both categories and context. Our very simple yet effective method is extensible to two multi-label tasks -- object and attribute classification. On 4 challenging datasets, we demonstrate the effectiveness of our method in reducing contextual bias.
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。
