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

Augmenting Images with Contextual Texture Cues to Assist Viewers with Color Vision Deficiency

This disclosure describes techniques that enable a viewer with color vision deficiency to better understand color images by automatically mapping problematic colors to distinguishable textures - distinctive black-and-white patterns corresponding to particular colors. A deep learning model selects appropriate textures for each color such that textures are consistent across images. Textures inform the viewer of similar and dissimilar colors and are only applied where needed. The original color image is augmented with textures such that both color-deficient users and users with regular vision can understand the image. The techniques can be used to display images on any device and can also be implemented as a printer plug-in to enable the printing of texture-augmented color images. The techniques can be implemented in web browsers, online image/video hosting services, and by any provider of color images. The techniques can also be used in smart glasses and augmented reality or virtual reality applications. By appropriately selecting the deep-learning network that generates the textures, the smart glasses can be used by multiple users with different types of color deficiencies

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

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