Stereoscopic Video Object Parsing by Multi-modal Transfer Learning – We propose a new class of 3D motion models for action recognition and video object retrieval based on visualizing objects in low-resolution images. Such 3D motion models are capable of capturing different aspects of the scene, such as pose, scale and lighting. These two aspects are not only pertinent when learning 3D object models, but could also be exploited for learning 2D objects as well. In this paper, we present a novel method called Multi-modal Motion Transcription (m-MNT) to encode spatial information in a new 3D pose space using deep convolutional neural networks. Such 3D data is used to learn both object semantic and pose variations of objects. We compare the performance of m-MNT on the challenging ROUGE 2017 dataset and the challenging 3D motion datasets such as WER and SLIDE. Our method yields competitive performance in terms of speed and accuracy; hence, the m-MNT class has a good future for action recognition.

In this paper, we investigate using the conditional probability method of Bernoulli and the Bayesian kernel calculus to derive the conditional probability methods of Bernoulli and the Bayesian kernel calculus for sparse Gaussian probability. Using such methods, we propose a conditional probability method of Bernoulli that is able to produce a sparse posterior and a conditional probability distributions over the Gaussian probability distributions. The conditional probability method is computationally efficient, as it can be applied to a mixture of Gaussian probability distributions generated by our method.

The Sigmoid Angle for Generating Similarities and Diversity Across Similar Societies

Sparse Multiple Instance Learning

Stereoscopic Video Object Parsing by Multi-modal Transfer Learning

Learning Visual Representations with Convolutional Neural Networks and Binary Networks for Video Classification

Efficiently Regularizing Log-Determinantal Point Processes: A General Framework and Completeness Querying ApproachIn this paper, we investigate using the conditional probability method of Bernoulli and the Bayesian kernel calculus to derive the conditional probability methods of Bernoulli and the Bayesian kernel calculus for sparse Gaussian probability. Using such methods, we propose a conditional probability method of Bernoulli that is able to produce a sparse posterior and a conditional probability distributions over the Gaussian probability distributions. The conditional probability method is computationally efficient, as it can be applied to a mixture of Gaussian probability distributions generated by our method.