A Simple and Efficient Method to Handle Sparse Preference Data Using Domination Graphs: An Application to YouTube

AbstractThe phenomenal growth of the number of videos on YouTube provides enormous potential for users to find content of interest to them. Unfortunately, as the size of the repository grows, the task of discovering high-quality content becomes more daunting. To address this, YouTube occasionally asks users for feedback on videos. In one such event (the YouTube Comedy Slam), users were asked to rate which of two videos was funnier. This yielded sparse pairwise data indicating a participant's relative assessment of two videos. Given this data, several questions immediately arise: how do we make inferences for uncompared pairs, overcome noisy, and usually contradictory, data, and how do we handle severely skewed, real-world, sampling? To address these questions, we introduce the concept of a domination-graph, and demonstrate a simple and scalable method, based on the Adsorption algorithm, to efficiently propagate preferences through the graph. Before tackling the publicly available YouTube data, we extensively test our approach on synthetic data by attempting to recover an underlying, known, rank-order of videos using similarly created sparse preference data

Finding Regions of Uncertainty in Learned Models: An Application to Face Detection

Abstract. After training statistical models to classify sets of data into predetermined classes, it is often di cult to interpret what the models have learned. This paper presents a novel approach for nding examples which lie on the decision boundaries of statistical models trained for classi cation. These examples provide insight into what the model has learned. Additionally, they can provide candidates for use as additional training data for improving the performance of the statistical models. By labeling the examples which lie on the decision boundaries, we provide information to the model in the regions in which it is most uncertain. The approaches presented in this paper are demonstrated on the real-world vision-based task of detecting faces in cluttered scenes.

Low-Bandwidth, Client-Based, Rendering for Gaming Videos

A system for low-bandwidth, client-based, rendering for gaming videos is described. The system may include a gaming device, server device, and user devices. The gaming device may include a processing device and graphics processing unit (GPU). The processing device receives user input and generates rendering commands from the user input. A first rendering unit of the GPU generates gaming video from the rendering commands. The server device receives the gaming video and the rendering commands from the gaming device. The server device determines the first user device is not compatible with the rendering commands, compresses the gaming video, and transmits the compressed gaming video to the first user device. The server device determines the second user device is compatible with the rendering commands and transmits the rendering commands to the second user device. The second rendering engine of the second user device generates rendered gaming video from the rendering commands

Dynamic relevance: vision-based focus of attention using artificial neural networks

AbstractThis paper presents a method for ascertaining the relevance of inputs in vision-based tasks by exploiting temporal coherence and predictability. In contrast to the tasks explored in many previous relevance experiments, the class of tasks examined in this study is one in which relevance is a time-varying function of the previous and current inputs. The method proposed in this paper dynamically allocates relevance to inputs by using expectations of their future values. As a model of the task is learned, the model is simultaneously extended to create task-specific predictions of the future values of inputs. Inputs that are not relevant, and therefore not accounted for in the model, will not be predicted accurately. These inputs can be de-emphasized, and, in turn, a new, improved, model of the task created. The techniques presented in this paper have been successfully applied to the vision-based autonomous control of a land vehicle, vision-based hand tracking in cluttered scenes, and the detection of faults in the plasma-etch step of semiconductor wafers

Placing Sponsored-Content Associated With An Image

Techniques are described for placing sponsored-content associated with an image. The techniques may include matching a first image for which a sponsored-content item is to be selected with a reference image. A sponsored-content item to be presented may be selected based on an association between the reference image and the sponsored-content item to be presented

Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision

Feature selection is essential for effective visual recognition. We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost unsupervised formulation. Our method requires only the following knowledge, which we call the \emph{feature sign}---whether or not a particular feature has on average stronger values over positive samples than over negatives. We show how this can be estimated using as few as a single labeled training sample per class. Then, using these feature signs, we extend an initial supervised learning problem into an (almost) unsupervised clustering formulation that can incorporate new data without requiring ground truth labels. Our method works both as a feature selection mechanism and as a fully competitive classifier. It has important properties, low computational cost and excellent accuracy, especially in difficult cases of very limited training data. We experiment on large-scale recognition in video and show superior speed and performance to established feature selection approaches such as AdaBoost, Lasso, greedy forward-backward selection, and powerful classifiers such as SVM.Comment: arXiv admin note: text overlap with arXiv:1411.771