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Attributes are visual concepts that can be detected by machines, understood by humans, and shared across categories. They are particularly useful for fine-grained domains where categories are closely related to one other (e.g. bird species recognition). In such scenarios, relevant attributes are often local (e.g. “white belly”), but the(More)
We introduce a new approach for part-based human pose estimation using multi-layer composite models, in which each layer is a tree-structured pictorial structure that models pose at a different scale and with a different graphical structure. At the highest level, the submodel acts as a person detector, while at the lowest level, the body is decomposed into(More)
Photo-sharing websites have become very popular in the last few years, leading to huge collections of online images. In addition to image data, these websites collect a variety of multimodal metadata about photos including text tags, captions, GPS coordinates, camera metadata, user profiles, etc. However, this metadata is not well constrained and is often(More)
In this paper, we present personalized service recommendation algorithm PSRA. Based on web service semantics, demographic factors and users' recommendation ratings and by redefining similarity measurement, PSRA presents different succeeding service recommendation towards the same current service to meet users' personalized needs. Experiment results show(More)
Vehicle recognition is a challenging task with many useful applications. State-of-the-art methods usually learn discriminative classifiers for different vehicle categories or different viewpoint angles, but little work has explored vehicle recognition using semantic visual attributes. In this paper, we propose a novel iterative multiple instance learning(More)
We report a case of acute massive cerebellar infarction associated with craniocervical junction (CVJ) complex malformation in a 21-year-old male. Timely surgical intervention prevented the deterioration of his neurological status.
Egocentric vision has received increasing attention in recent years due to the vast development of wearable devices and their applications. Although there are numerous existing work on egocentric vision, none of them solve Optical Music Recognition (OMR) problem. In this paper, we propose a novel optical music recognition approach for egocentric device(More)
In this paper, our goal is to speed up a standard sliding window detector while maintaining detection accuracies. We do this by decomposing its weight template into multiple component detectors with no redundancy. Each component detector captures partial discriminative appearance, and they can be used in a cascade detection pipeline to aggressively reduce(More)