Show and Tell!

Show and Tell! – We present and evaluate a new algorithm for learning a function from a set of noisy image patches. The key idea behind the algorithm is to reduce the training error and the training set to a minimal set of noisy image patches. We demonstrate that the algorithm significantly improves the performance of our algorithm compared to a simple image restoration technique.

In this paper we propose an efficient and robust approach for the problem of image segmentation. We analyze the image segmentation models using their information density properties, and propose a new algorithm which is based on Bayesian network (BCN) for this problem. The proposed BCN is fast and simple compared to previous methods that focus on learning the data from multiple sources. We demonstrate the advantages of the proposed approach by a supervised benchmark for the problem. The results show that the proposed algorithm is robust to noisy inputs and fails in the noisy segmentation models, and can perform as well in the noisy segmentation models, as the previous method did.

Understanding the interplay of a sequence of motion sequences can be an important resource for autonomous vehicle navigation. One of the challenges in tracking such a sequence is that the motion is not accurately captured, i.e., the time is too short or long to allow proper tracking. In this paper, we propose a learning based method for tracking motion sequences. A tracking network is trained with a video sequence, and a set of objects is automatically captured by a robot. The robot then tracks objects in the video sequence. As an end-to-end learning method, our method requires a video-based data augmentation method. The learning method is applied to three different tracking strategies: tracking motion sequences without data augmentation, tracking motion sequences without video augmentation, and tracking motion sequences without video augmentation. The results show that our approach significantly outperforms the previous methods on a variety of tracking scenarios without data augmentation.

A Bayesian Framework for Sparse Kernel Contrastive Filtering

The Data Science Approach to Empirical Risk Minimization

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  • On the Geometry of Color Transfer Discriminative CNN Classifiers and Deep Residual Networks for Single image Super-resolution

    A Framework for Interactive Vehicle Detection and Localization in Video with Event-Part InteractionsUnderstanding the interplay of a sequence of motion sequences can be an important resource for autonomous vehicle navigation. One of the challenges in tracking such a sequence is that the motion is not accurately captured, i.e., the time is too short or long to allow proper tracking. In this paper, we propose a learning based method for tracking motion sequences. A tracking network is trained with a video sequence, and a set of objects is automatically captured by a robot. The robot then tracks objects in the video sequence. As an end-to-end learning method, our method requires a video-based data augmentation method. The learning method is applied to three different tracking strategies: tracking motion sequences without data augmentation, tracking motion sequences without video augmentation, and tracking motion sequences without video augmentation. The results show that our approach significantly outperforms the previous methods on a variety of tracking scenarios without data augmentation.


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