面向开放世界无偏检测器的提议级无监督域适应
Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector
AI总结：
针对开放世界目标检测中前景预测器偏向已知类别、未见类别外观偏移下失效的问题，提出将任务重构为提议级无监督域适应，通过自训练学习域不变特征实现无偏检测，达到当前最优性能。
摘要：
开放世界目标检测（Open World Object Detection, OWOD）将开集目标检测与增量学习能力相结合，以应对开放动态视觉世界的挑战。现有工作假设，在已见类别上训练的前景预测器可通过选取置信度最高的前k个前景预测结果，直接迁移用于识别未见类别的位置。然而该假设在实践中几乎不成立，原因是预测器不可避免地偏向已知类别，且在未见类别外观发生偏移时会失效。本研究旨在通过在无监督域适应（Unsupervised Domain Adaptation, UDA）框架下重构该任务，构建无偏前景预测器：由当前有偏预测器辅助划分域，将已见目标位置和高置信度背景位置作为源域，其余模糊位置作为目标域。随后我们采用简单有效的自训练方法，基于域不变前景特征学习预测器，从而实现对已见与未见类别间外观偏移具有鲁棒性的无偏预测。该方法的流程可适配多种检测框架与UDA方法，经OWOD评估实验验证，我们取得了当前最优性能。
英文摘要：
Open World Object Detection (OWOD) combines open-set object detection with incremental learning capabilities to handle the challenge of the open and dynamic visual world. Existing works assume that a foreground predictor trained on the seen categories can be directly transferred to identify the unseen categories' locations by selecting the top-k most confident foreground predictions. However, the assumption is hardly valid in practice. This is because the predictor is inevitably biased to the known categories, and fails under the shift in the appearance of the unseen categories. In this work, we aim to build an unbiased foreground predictor by re-formulating the task under Unsupervised Domain Adaptation, where the current biased predictor helps form the domains: the seen object locations and confident background locations as the source domain, and the rest ambiguous ones as the target domain. Then, we adopt the simple and effective self-training method to learn a predictor based on the domain-invariant foreground features, hence achieving unbiased prediction robust to the shift in appearance between the seen and unseen categories. Our approach's pipeline can adapt to various detection frameworks and UDA methods, empirically validated by OWOD evaluation, where we achieve state-of-the-art performance.
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。
