Weakly supervised object localization and segmentation in videos

We consider the problem of localizing and segmenting objects in weakly labeled video. A video is weakly labeled if it is associated with a tag (e.g. YouTube videos with tags) describing the main object present in the video. It is weakly labeled because the tag only indicates the presence/absence of the object, but does not give the detailed spatial/temporal location of the object in the video. Given a weakly labeled video, our method can automatically localize the object in each frame and segment it from the background. Our method is fully automatic and does not require any user-input. In principle, it can be applied to a video of any object class. We evaluate our proposed method on a dataset with more than 100 video shots. Our experimental results show that our method outperforms other baseline approaches.