  This paper presents a novel framework in which video/image segmentation and
localization are cast into a single optimization problem that integrates
information from low level appearance cues with that of high level localization
cues in a very weakly supervised manner. The proposed framework leverages two
representations at different levels, exploits the spatial relationship between
bounding boxes and superpixels as linear constraints and simultaneously
discriminates between foreground and background at bounding box and superpixel
level. Different from previous approaches that mainly rely on discriminative
clustering, we incorporate a foreground model that minimizes the histogram
difference of an object across all image frames. Exploiting the geometric
relation between the superpixels and bounding boxes enables the transfer of
segmentation cues to improve localization output and vice-versa. Inclusion of
the foreground model generalizes our discriminative framework to video data
where the background tends to be similar and thus, not discriminative. We
demonstrate the effectiveness of our unified framework on the YouTube Object
video dataset, Internet Object Discovery dataset and Pascal VOC 2007.
