Google Inc.

Facebook, Inc.

Junfeng He

Dmitry Lagun

Pingmei Xu

Shanmugasundaram Ravikumar

Nachiappan Valliappan

Michael Rubinstein

Kathryn Hymes

Ariel Benjamin Evnine

Sadi Sufi Khan

Inbar Mosseri

Christina Joan Sauper

Pasin Manurangsi

Zhongxian Chen

Yael Pritch Knaan

David Edward Jacobs

Kfir Aberman

Christine Morck Rode

Yossi Gandelsman

Kai Jochen Kohlhoff

Tuhin Kumar

Josue Perez

Improved methods are provided for generating heatmaps or other summary map data from multiple users' data (e.g., probability distributions) in a manner that preserves the privacy of the users' data while also generating heatmaps that are visually similar to the 'true' heatmap. These methods include decomposing the average of the users' data (the ‘true’ heatmap) into multiple different spatial scales, injecting random noise into the data at the multiple different spatial scales, and then reconstructing the privacy-preserving heatmap based on the noisy multi-scale representations. The magnitude of the noise injected at each spatial scale is selected to ensure preservation of privacy while also resulting in heatmaps that are visually similar to the ‘true’ heatmap.

Differentially private heatmaps

Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.

Techniques for reducing a distraction in an image

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for characterizing a gaze position of a user in a query image. One of the methods includes obtaining a query image of a user captured by a camera of a mobile device; obtaining device characteristics data specifying (ii) characteristics of the mobile device, (ii) characteristics of the camera of the mobile device, or (iii) both; and processing a neural network input comprising (i) one or more images derived from the query image and (ii) the device characteristics data using a gaze prediction neural network, wherein the gaze prediction neural network is configured to, at run time and after the gaze prediction neural network has been trained, process the neural network input to generate a neural network output that characterizes a gaze position of the user in the query image.

Eye gaze tracking using neural networks

Exemplary methods, apparatuses, and systems for processing a search query of a user are detailed. For example, a search query may be received from a user at a social networking system, processed to generate a search result of a plurality of entity result cards and each result card that each include a plurality of order comments about the entity, a plurality of ordered images associated with the entity, contact information for the entity, wherein the comments and images take into account information about the user stored at the social networking system, and a result send to the user.

Systems, methods, and apparatuses for performing search queries

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Fremont, CA, United States of America

Junfeng He