Abstract: Systems and techniques are generally described for generating visually blended recommendation grids. In some examples, a selection of a first item and a second item displayed on a display may be received. In various examples, the first item may be displayed in a first element of a grid and the second item may be displayed in a second element of the grid. In some examples, a third element of the grid that is disposed between the first element and the second element along an axis of the grid may be determined. In various examples, a third item may be determined for display in the third element of the grid based at least in part on a blended representation of an embedding of the first item and an embedding of the second item. The third item may be displayed in the third element of the grid.

Abstract: A method for learning deep convolutional features specifically designed for correlation filter based visual tracking includes the steps of, selecting a first image from a first image patch; selecting a second image from a second image patch; forward propagating selected first image by a convolutional neural network model formula, the formula has random weights with zero mean for the parameters; forward propagating selected second image by the convolutional neural network model formula; computing correlation filter using forward propagated second image and centered correlation response; circularly correlating forward propagated first image and computed correlation filter to generate predicted response map; calculating the loss by comparing the predicted response map with desired correlation corresponding selected first image and second image and updating the parameters of the convolutional neural network model formula according to calculated loss.

Abstract: A method for learning deep convolutional features specifically designed for correlation filter based visual tracking includes the steps of, selecting a first image from a first image patch; selecting a second image from a second image patch; forward propagating selected first image by a convolutional neural network model formula, the formula has random weights with zero mean for the parameters; forward propagating selected second image by the convolutional neural network model formula; computing correlation filter using forward propagated second image and centered correlation response; circularly correlating forward propagated first image and computed correlation filter to generate predicted response map; calculating the loss by comparing the predicted response map with desired correlation corresponding selected first image and second image and updating the parameters of the convolutional neural network model formula according to calculated loss.