Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling

TL;DR

This paper introduces Mask Pooling, a novel method that reduces context bias in domain adaptation for object detection by separating foreground and background pooling, leading to more robust detection across domains.

Contribution

The paper provides a causal analysis of context bias and proposes Mask Pooling as a principled solution to improve domain robustness in object detection models.

Findings

Mask Pooling improves detection robustness across domains.

A new benchmark tests models with random backgrounds.

Mask Pooling reduces reliance on background context.

Abstract

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.