Domain Adaptive Object Detection (DAOD) models a joint distribution of images
and labels from an annotated source domain and learns a domain-invariant
transformation to estimate the target labels with the given target domain
images. Existing methods assume that the source domain labels are completely
clean, yet large-scale datasets often contain error-prone annotations due to
instance ambiguity, which may lead to a biased source distribution and severely
degrade the performance of the domain adaptive detector de facto. In this
paper, we represent the first effort to formulate noisy DAOD and propose a
Noise Latent Transferability Exploration (NLTE) framework to address this
issue. It is featured with 1) Potential Instance Mining (PIM), which leverages
eligible proposals to recapture the miss-annotated instances from the
background; 2) Morphable Graph Relation Module (MGRM), which models the
adaptation feasibility and transition probability of noisy samples with
relation matrices; 3) Entropy-Aware Gradient Reconcilement (EAGR), which
incorporates the semantic information into the discrimination process and
enforces the gradients provided by noisy and clean samples to be consistent
towards learning domain-invariant representations. A thorough evaluation on
benchmark DAOD datasets with noisy source annotations validates the
effectiveness of NLTE. In particular, NLTE improves the mAP by 8.4\% under 60\%
corrupted annotations and even approaches the ideal upper bound of training on
a clean source dataset.

本研究提出一种面向带有噪声注释的领域自适应目标检测（DAOD）的框架，包括潜在实例挖掘、可变形图关系模块和熵感知梯度协调等关键技术，将语义信息纳入学习过程，取得了不错的实验成果。