论文标题
不要以其上下文来判断对象：学会克服上下文偏见
Don't Judge an Object by Its Context: Learning to Overcome Contextual Bias
论文作者
论文摘要
现有模型通常利用对象及其上下文之间的共发生，以提高识别精度。但是，强烈依靠上下文风险模型的普遍性，尤其是在没有典型的共发生模式的情况下。这项工作着重于解决这种上下文偏见，以改善学习特征表示的鲁棒性。我们的目标是在没有上下文的情况下准确地识别一个类别，而不会在与上下文共同相处时的性能。我们的关键思想是从其共同存在的上下文中去脱异性特征表示。我们通过学习一个特征子空间来实现这一目标，该功能子空间明确表示在没有上下文的情况下出现的类别，而侧面是代表类别和上下文的联合特征子空间。我们非常简单但有效的方法可以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中，我们证明了方法在减少上下文偏见方面的有效性。
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.
