  Weakly supervised object localization (WSOL) focuses on localizing objects
only with the supervision of image-level classification masks. Most previous
WSOL methods follow the classification activation map (CAM) that localizes
objects based on the classification structure with the multi-instance learning
(MIL) mechanism. However, the MIL mechanism makes CAM only activate
discriminative object parts rather than the whole object, weakening its
performance for localizing objects. To avoid this problem, this work provides a
novel perspective that models WSOL as a domain adaption (DA) task, where the
score estimator trained on the source/image domain is tested on the
target/pixel domain to locate objects. Under this perspective, a DA-WSOL
pipeline is designed to better engage DA approaches into WSOL to enhance
localization performance. It utilizes a proposed target sampling strategy to
select different types of target samples. Based on these types of target
samples, domain adaption localization (DAL) loss is elaborated. It aligns the
feature distribution between the two domains by DA and makes the estimator
perceive target domain cues by Universum regularization. Experiments show that
our pipeline outperforms SOTA methods on multi benchmarks. Code are released at
\url{https://github.com/zh460045050/DA-WSOL_CVPR2022}.
