Deep-Person Recognition: A Benchmark – This paper investigates the effectiveness of a novel method for the automatic detection of human-body interactions (including a facial pose) in action sequences. The method is based on the assumption that the human action sequence is part of an action sequence and is characterized by human actions during the sequence. The model is able to capture the human body poses within a context of the action sequence and can accurately detect and distinguish persons of multiple identities. We present a novel method for automatic human pose estimation from the human-body interaction dataset to date. The proposed method is trained on a well-established human model (using a human subject) and tested on a set of large-scale 3D human pose datasets. The proposed method is able to achieve accuracies comparable to human pose estimation under the same training regime using only human body pose data and a human face data.
This paper describes a technique for learning a probabilistic model for uncertain data. This model predicts some unknowns of an unknown sample. The prediction can be easily computed using a probability measure and also is accurate to be used as a tool for decision makers in a machine learning system. This probabilistic model has been used to classify data from multiple applications, and has been used for decision analysis and to assess the modelability of the model.
Video Games: Human Decision and Learning
Deep-Person Recognition: A Benchmark
On-line learning of spatiotemporal patterns using an exact node-distance approach
Learning Bayesian Networks from Data with Unknown Labels: Theories and ExperimentsThis paper describes a technique for learning a probabilistic model for uncertain data. This model predicts some unknowns of an unknown sample. The prediction can be easily computed using a probability measure and also is accurate to be used as a tool for decision makers in a machine learning system. This probabilistic model has been used to classify data from multiple applications, and has been used for decision analysis and to assess the modelability of the model.