Amazon Technologies, Inc.

Superfish Ltd.

Alexander Lorbert

Arik Poznanski

Eduard Oks

Michael Chertok

David Neiman

Sharon Alpert

Adi Pinhas

Bar Hilleli

Assaf Neuberger

Gabi Shalev

Megan E Anderson

Layne Skullerud

Techniques are generally described for displaying outfit recommendations using a recurrent neural network. In various examples, a computing device may receive a first state vector representing an outfit comprising at least one fashion item. First image data depicting a second fashion item of a first item category may be received. A recurrent neural network may generate a first output feature vector based on the first state vector, the first image data, a first attribute vector, and the first item category. The first output feature vector may be compared to other feature vectors representing other fashion items in the first category to determine distances between the first output feature vector and the other feature vectors. A set of fashion items may be recommended and displayed based on the distances between the first output feature vector and the other feature vectors.

Real-time interactive outfit recommendation

Techniques are generally described for performing outfit recommendation using a recurrent neural network. In various examples, a computing device may receive a first state vector representing an outfit comprising at least one fashion item. First image data depicting a second fashion item of a first item category may be received. A machine learning mode may generate a first output feature vector based on the first state vector, the first image data, a first attribute vector, and the first item category. The first output feature vector may be compared to other feature vectors representing other fashion items in the first category to determine distances between the first output feature vector and the other feature vectors. A set of fashion items may be recommended based on the distances between the first output feature vector and the other feature vectors.

Attribute-aware outfit recommendation

First image data representing a first human wearing a first article of clothing may be received. The first image data, when rendered on a display, may include a first photometric artifact. A first generator network may be used to generate second image data from the first image data. The first photometric artifact may be removed from the second image data. A second generator network may be used to generate third image data from the second image data, the third image data representing the first human in a different pose relative to the first image data. Fourth image data representing the first article of clothing segmented from the first human may be generated and displayed on a display.

Catalog normalization and segmentation for fashion images

Techniques are generally described for performing outfit recommendation using a recurrent neural network. In various examples, a computing device may receive a first state vector representing an outfit comprising at least one article of clothing. First image data depicting a second article of clothing of a first clothing category may be received. A recurrent neural network may generate a first output feature vector based on the first state vector, the first image data and the first clothing category. The first output feature vector may be compared to other feature vectors representing other articles of clothing in the first category to determine distances between the first output feature vector and the other feature vectors. A set of articles of clothing may be recommended based on the distances between the first output feature vector and the other feature vectors.

Outfit recommendation using recurrent neural networks

A method for producing a graph representation of an input image, the method including the procedures of applying convolutional layers of a trained convolutional neural network on the input image, defining a receptive field of a last convolutional layer of the trained convolutional neural network as a vertex of the graph representation, defining a vector of a three dimensional output matrix of the last convolutional layer that is mapped to the receptive field as a descriptor for the vertex and determining an edge between a pair of vertices of the graph representation by applying an operator on a pair of descriptors respective of the pair of vertices.

Graph image representation from convolutional neural networks

A method for representing an input image includes the steps of applying a trained neural network on the input image, selecting a plurality of feature maps, determining a location of each of the plurality of feature maps in an image space of the input image, defining a plurality of interest points of the input image, and employing the plurality of interest points for representing the input image for performing a visual task. The plurality of feature maps are selected of an output of at least one selected layer of the trained neural network according to values attributed to the plurality of feature maps by the trained neural network. The plurality of interest points of the input image are defined based on the locations corresponding to the plurality of feature maps.

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Giv′at-Shmuel, Israel

Alexander Lorbert