We present a solution to the problem of privacy invasion in a multiparty digital rights management scheme. (Roaming) users buy content licenses from a content provider and execute it at any nearby content distributor. Our approach, which does not need any trusted third party—in contrast to most related work on privacy-preserving DRM—is based on a re-encryption scheme that runs on any mobile And...

We present a framework that automatically and quickly learns a large number of signs from sign language-interpreted TV broadcasts by exploiting supervisory information available in the subtitles. Our contributions are: (i) we show that, somewhat counter-intuitively, mouth patterns are highly informative for distinguishing words in a language for the Deaf, and their co-occurrence with signing ca...

Object detection in large-scale real-world scenes requires efficient multi-class detection approaches. Random forests have been shown to handle large training datasets and many classes for object detection efficiently. The most prominent example is the commercial application of random forests for gaming [37]. In this paper, we describe the general framework of random forests for multi-class obj...

Confounded information is an objective fact when using multi-instance learning (MIL) to classify bags of instances, which may be inherited by MIL embedding methods and lead questionable bag label prediction. To respond this problem, we propose the with deconfounded instance-level prediction algorithm. Unlike traditional embedding-based strategies, design a optimization goal maximize distinction...