Microsoft Technology Licensing, LLC

Mithun Das Gupta

Manish Gupta

Prashant Gupta

Sudhir Kumar

Soudamini Sreepada

Rushabh Rajesh Gandhi

Arun Sacheti

Kun Wu

Houdong Hu

Naresh Annam

Shashank Verma

Yiran Shen

Jagjot Singh

A computer-implemented technique is described herein for using a machine-trained model to identify individual objects within images. The technique then creates a relational index for the identified objects. That is, each index entry in the relational index is associated with a given object, and includes a set of attributes pertaining to the given object. One such attribute identifies at least one latent semantic vector associated with the given object. Each attribute provides a way of linking the given object to one or more other objects in the relational index. In one application of this technique, a user may submit a query that specifies a query object. The technique consults the relational index to find one or more objects that are related to the query object. In some cases, the query object and each of the other objects have a complementary relationship.

Generating and applying an object-level relational index for images

A computer-implemented technique is described herein for retrieving at least one recommended output image. In one implementation, the technique uses a generator component to transform first-part image information, associated with a first-part image selected by a user, into one or more instances of second-part generated image information. Each instance of the second-part generated image information complements the first-part image information. The generator component is trained by a computer-implemented training system using a conditional generative adversarial network (cGAN). The technique further includes: retrieving one or more second-part output images from a data store based on the instance(s) of second-part generated image information; generating a user interface presentation that presents the first-part image and the second-part output image(s); and displaying the user interface presentation on a display device. In one example, the first-part image and the second-part output images show complementary apparel items.

Finding complementary digital images using a conditional generative adversarial network

Techniques for automating the generation and analysis of fundus drawings are described. Captured images undergo image processing to extract information about image features. Fundus images are generated and recommended labels for the fundus drawing are generated. Fundus drawings can be analyzed and undergo textual processing to extract existing labels. Machine learning models and co-occurrence analysis can be applied to collections of fundus images and drawings to gather information about commonly associated labels, label locations, and user information. The most frequently used labels associated with the image can be identified to improve recommendations and personalize labels.

Automatic generation of fundus drawings

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Hyderabad Telangana, India

Mithun Das Gupta