Learning to Detect Small Signs from Large Images


Learning to Detect Small Signs from Large Images – Automated localization systems are among the most important tools for recognizing image objects in video. Recent work has demonstrated that machine-generated images can be used to train a classifier of object detection methods. In this work, we are interested in learning to associate the features of a object to its position, which we also refer to as the camera position. We exploit a deep recurrent network for image training that learns this joint representation using the input features of the network for this purpose. Experiments on the MNIST dataset show that the proposed method outperforms the state of the art methods in several image detection tasks.

Anomaly detection and correction for the online video generation is a vital problem in the areas of computer vision, natural language processing and video analysis. In this paper we propose to perform anomaly detection and correction in real-time using a novel distributed learning pipeline. We use a distributed stochastic gradient estimator to compute a posterior of a video model using the Gaussian process (GP) model. We demonstrate that the proposed approach outperforms the state-of-the-art anomaly detection and correction methods.

Tensor Logistic Regression via Denoising Random Forest

Viewpoint Improvements for Object Detection with Multitask Learning

Learning to Detect Small Signs from Large Images

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  • An iterative k-means method for minimizing the number of bound estimates

    Distributed Online Learning: A Bayesian ApproachAnomaly detection and correction for the online video generation is a vital problem in the areas of computer vision, natural language processing and video analysis. In this paper we propose to perform anomaly detection and correction in real-time using a novel distributed learning pipeline. We use a distributed stochastic gradient estimator to compute a posterior of a video model using the Gaussian process (GP) model. We demonstrate that the proposed approach outperforms the state-of-the-art anomaly detection and correction methods.


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