This paper addresses the problem of learning object models from egocentric video of household activities, using extremely weak supervision. For each activity sequence, we know onl...
: A new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. We assume that each object can be characterized by a se...
Andreas Opelt, Axel Pinz, Michael Fussenegger, Pet...
A new strategy is proposed for the design of cascaded object detectors of high detection-rate. The problem of jointly minimizing the false-positive rate and classification complex...
Recognizing categories of articulated objects in real-world scenarios is a challenging problem for today's vision algorithms. Due to the large appearance changes and intra-cla...
We present a video summarization approach for egocentric or “wearable” camera data. Given hours of video, the proposed method produces a compact storyboard summary of the came...