Amazon Technologies, Inc.

Matthieu Guillaumin

Nikhil Garg

Michael Donoser

Zohar Barzelay

Brad Bowman

Bojan Pepik

Devices and techniques are generally described for retrieving three dimensional part models. Two-dimensional (2D) image data representing an object with at least a first part and a second part may be received. A first machine learning model may be used to generate a first shape embedding representing the first part and a second shape embedding representing the second part. A first 3D model stored in a non-transitory computer-readable memory that represents the first part may be determined based at least in part on the first shape embedding. A second 3D model stored in the non-transitory computer-readable memory that represents the second part may be determined based at least in part on the second shape embedding.

Three dimensional object part retrieval

Techniques for prioritizing images associated with an item to display an appropriate image based on a query are described herein. For example, an attention score for an item attribute in an image of an item may be generated based at least in part on a model that uses one or more images of the item. The item attribute for the item associated with a query may be obtained. A plurality of items may be determined based at least in part on the item attribute being associated with the plurality of items where an individual item of the plurality of items includes a plurality of images of the item. The plurality of images of the individual item may be ranked based at least in part on corresponding attention scores associated with each image of the plurality of images.

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Berlin, Germany

Matthieu Guillaumin