A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation – Person re-identification is an important problem in many areas including robotics and artificial intelligence. In this paper, we investigate the challenge in Re-ID for the purpose of re-identification of the human-body connection from images. Following the previous work on this problem, we propose a novel two-phase re-identification algorithm based on the idea of re-scented image classification and localization. Under this framework, image re-ID is used to classify the human-body connection between the images. This paper considers re-ID as a supervised model which can easily be designed to re-identify the person and the person re-ID. The proposed re-ID algorithm is implemented using ImageNet, which handles image classification and localization for a semi-automated test and evaluation system. Furthermore, it is implemented using a machine learning framework which handles the classification and localization for an automatic re-ID system.
We present a novel approach to detect large-scale object detection from unlabeled video images. Instead of training a deep convolutional network to learn to detect specific objects, we train a neural network to learn to recognize more salient features from unlabeled videos. Experimental results show that our approach significantly outperforms previous methods on the challenging PASCAL VOC dataset collected from an urban neighborhood.
A Generalized Baire Gradient Method for Gaussian Graphical Models
Adversarial Retrieval with Latent-Variable Policies
A Semi-automated Test and Evaluation System for Multi-Person Pose Estimation
Using Deep Learning to Detect Multiple Paths to Plagas
Learning an RGBD Model of a Moving Object using Deep LearningWe present a novel approach to detect large-scale object detection from unlabeled video images. Instead of training a deep convolutional network to learn to detect specific objects, we train a neural network to learn to recognize more salient features from unlabeled videos. Experimental results show that our approach significantly outperforms previous methods on the challenging PASCAL VOC dataset collected from an urban neighborhood.