提出遮罩池化方法，减少目标检测中背景干扰带来的偏差
Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling
- 用前景遮罩指导池化操作，分离前景与背景特征提取
- 在随机背景测试下显著提升模型跨域检测鲁棒性
- 为领域自适应检测提供可解释的因果分析框架
上下文偏差指目标检测训练过程中前景物体与背景之间的错误关联。现有领域自适应目标检测（DAOD）方法虽试图缓解此问题，但缺乏对偏差成因的系统理解。本文从因果视角揭示：卷积网络中的池化操作是造成该偏差的潜在根源。为此，提出一种新型遮罩池化（Mask Pooling）方法，引入前景遮罩作为额外输入，分别对前景和背景区域进行独立池化，使模型在不同域间具备更强的泛化能力。此外，构建一个基准测试，采用绝对随机背景下的前景数据，以极端条件评估模型鲁棒性。实验表明，该方法能有效降低上下文偏差，为应对领域偏移提供可解释的解决方案。
原文摘要 · Abstract (English)
Context bias refers to the association between the foreground objects and background during the object detection training process. Various methods have been proposed to minimize the context bias when applying the trained model to an unseen domain, known as domain adaptation for object detection (DAOD). But a principled approach to understand why the context bias occurs and how to remove it has been missing. In this work, we provide a causal view of the context bias, pointing towards the pooling operation in the convolution network architecture as the possible source of this bias. We present an alternative, Mask Pooling, which uses an additional input of foreground masks, to separate the pooling process in the respective foreground and background regions and show that this process leads the trained model to detect objects in a more robust manner under different domains. We also provide a benchmark designed to create an ultimate test for DAOD, using foregrounds in the presence of absolute random backgrounds, to analyze the robustness of the intended trained models. Through these experiments, we hope to provide a principled approach for minimizing context bias under domain shift.
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