One Shot Joint Colocalization and Cosegmentation
This work addresses the challenge of simultaneously segmenting and localizing objects in images for computer vision applications, representing an incremental improvement over existing methods.
The paper tackles the joint problem of image cosegmentation and colocalization by integrating low-level appearance and high-level localization cues in a weakly supervised optimization framework, outperforming baseline approaches by a large margin on four benchmark datasets and achieving competitive results on others.
This paper presents a novel framework in which image cosegmentation and colocalization 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. In contrast to multi-task learning paradigm that learns similar tasks using a shared representation, the proposed framework leverages two representations at different levels and simultaneously discriminates between foreground and background at the bounding box and superpixel level using discriminative clustering. We show empirically that constraining the two problems at different scales enables the transfer of semantic localization cues to improve cosegmentation output whereas local appearance based segmentation cues help colocalization. The unified framework outperforms strong baseline approaches, of learning the two problems separately, by a large margin on four benchmark datasets. Furthermore, it obtains competitive results compared to the state of the art for cosegmentation on two benchmark datasets and second best result for colocalization on Pascal VOC 2007.