  While remarkable success has been achieved in weakly-supervised object
localization (WSOL), current frameworks are not capable of locating objects of
novel categories in open-world settings. To address this issue, we are the
first to introduce a new weakly-supervised object localization task called
OWSOL (Open-World Weakly-Supervised Object Localization). During training, all
labeled data comes from known categories and, both known and novel categories
exist in the unlabeled data. To handle such data, we propose a novel paradigm
of contrastive representation co-learning using both labeled and unlabeled data
to generate a complete G-CAM (Generalized Class Activation Map) for object
localization, without the requirement of bounding box annotation. As no class
label is available for the unlabelled data, we conduct clustering over the full
training set and design a novel multiple semantic centroids-driven contrastive
loss for representation learning. We re-organize two widely used datasets,
i.e., ImageNet-1K and iNatLoc500, and propose OpenImages150 to serve as
evaluation benchmarks for OWSOL. Extensive experiments demonstrate that the
proposed method can surpass all baselines by a large margin. We believe that
this work can shift the close-set localization towards the open-world setting
and serve as a foundation for subsequent works. Code will be released at
https://github.com/ryylcc/OWSOL.
