  This work addresses the task of class-incremental weakly supervised object
localization (CI-WSOL). The goal is to incrementally learn object localization
for novel classes using only image-level annotations while retaining the
ability to localize previously learned classes. This task is important because
annotating bounding boxes for every new incoming data is expensive, although
object localization is crucial in various applications. To the best of our
knowledge, we are the first to address this task. Thus, we first present a
strong baseline method for CI-WSOL by adapting the strategies of
class-incremental classifiers to mitigate catastrophic forgetting. These
strategies include applying knowledge distillation, maintaining a small data
set from previous tasks, and using cosine normalization. We then propose the
feature drift compensation network to compensate for the effects of feature
drifts on class scores and localization maps. Since updating network parameters
to learn new tasks causes feature drifts, compensating for the final outputs is
necessary. Finally, we evaluate our proposed method by conducting experiments
on two publicly available datasets (ImageNet-100 and CUB-200). The experimental
results demonstrate that the proposed method outperforms other baseline
methods.
