Figure Eight Technologies, Inc.

Novumind Limited

Monchu Chen

Robert J Munro

Kiran Vajapey

Caiqun Xiao

Seyyedeh Qazale Mirsharif

Humayun Irshad

Joseph Richard Cloughley

Matthew Allen Gordon

Image annotation includes: accessing initial object prediction information associated with an image, wherein the initial object prediction information includes a plurality of initial predictions associated with a plurality of objects in the image, including bounding box information associated with the plurality of objects; presenting the image and at least a portion of the initial object prediction information to be displayed; receiving adjusted object prediction information pertaining to at least some of the plurality of objects, wherein the adjusted object prediction information is obtained from a user input made via a user interface configured for a user to make annotation adjustments to at least some of the initial object prediction information; and outputting updated object prediction information, wherein the updated object prediction information is based at least in part on the adjusted object prediction information.

Assisted image annotation

A technique is disclosed for automating tracking of annotated objects and improves the throughput and efficiency of existing methods while maintaining a degree of accuracy comparable to a human annotator. In particular, the disclosed technique provides an automated annotated object tracking tool that allows machine-learning teams to annotate an object within a frame and have that annotation persist across frames as the annotated object is tracked within a series of frames, still ensuring that every frame is accurately reviewed by a human where high quality annotation is required. This technique incorporates human feedback via a user adjustment that allows the tool to adapt and improve its accuracy in tracking an annotated object across a sequence of frames.

Video object tracking

Image annotation includes: accessing an image and a plurality of annotation data sets for the image, wherein the plurality of annotation data sets are made by a plurality of contributors, and the image has a plurality of original image channels; aggregating the plurality of annotation data sets to obtain an aggregated annotation data set for the image; and outputting the aggregated annotation data set. Aggregating the plurality of annotation data sets to obtain an aggregated annotation data set for the image includes: generating an additional image channel based at least in part on weight averages of confidence measures of the plurality of contributors; and applying an object detection model to at least a part of the plurality of original image channels and at least a part of the additional image channel to generate the aggregated annotation data set.

Aggregated image annotation

A computer system may provide Encrypted Deep Learning Service (EDLS) to a client. The computer system includes one or more processors and memory storing instructions. When instructions are executed by the one or more processors, the instructions cause the computer system to perform acts including: receiving training data from the client, where the training data comprise cipher images that are encrypted using an orthogonal transformation that hides sensitive information in original images. The acts further include training a deep neural network using the training data in the computer system; and producing cipher inference using the deep neural network when the computer system receives new data including new images encrypted using the orthogonal transformation.

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San Francisco, CA, United States of America

Monchu Chen