Baopeng Zhang

TL;DR: This paper consists of the following contributions: massive social images and their privacy settings are leveraged to learn the object-privacy relatedness effectively and identify a set of privacy-sensitive object classes automatically and a deep multi-task learning algorithm is developed.

TL;DR: Both the image content sensitiveness and the user trustworthiness are integrated to train a tree classifier to recommend fine-grained privacy settings for social image sharing.

TL;DR: By learning the tree classifier, the deep network and the visual hierarchy adaptation jointly in an end-to-end manner, the LMM algorithm can achieve higher accuracy rates on hierarchical visual recognition.

TL;DR: The proposed railway detection method is evaluated on a number of challenging images and experiments demonstrate that the proposed method is an effective and detailed solution to railway detection, and is superior to other railway detection methods.

TL;DR: A deep spatial and temporal network (DSTN) is developed for visual tracking by explicitly exploiting both the object representations from each frame and their dynamics along multiple frames in a video, such that it can seamlessly integrate the object appearances with their motions to produce compact object appearances and capture their temporal variations effectively.