Five Ai Limited

Amazon Technologies, Inc.

Sina Samangooei

John Redford

David Spike Palfrey

Mihai Valentin Tablan

Petra Elisabeth Holmes

Ying Chan

Arpit Mittal

Thomas Westmacott

Sebastian Kaltwang

Robert Chandler

Andrew Graham Fenn

Blaine Rogers

Benjamin Fuller

Benjamin David Scott

Emilio Fabio Monti

Dianhuan Lin

William Froom

Maxime Peyrard

Jamie Cruickshank

Stuart Golodetz

Pedro Ribeiro

Luca Bertinetto

Romain Mueller

Konstantinos Tertikas

Nicholas A Lord

Tommaso Cavallari

A computer-implemented method of creating 2D annotation data for annotating one or more perception inputs comprises: receiving at the annotation computer system at least one captured frame comprising a set of 3D structure points, in which at least a portion of a structure component is captured; computing a reference position for the structure component within the frame; generating a 3D model for the structure component by selectively extracting 3D structure points of the frame based on the reference position; computing a projection of the 3D model into an image plane; and storing 2D annotation data of the computed projection in persistent computer storage for annotating the structure component within the image plane.

Structure annotation

Herein, a 'perception statistical performance model' (PSPM) for modelling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A first PSPM is configured to: receive a computed perception ground truth; determine from the perception ground truth, based on a set of learned parameters, a probabilistic perception uncertainty distribution, the parameters learned from a set of actual perception outputs generated using the perception slice to be modelled, in order to compute a first time series of perception outputs. A second time series of perception outputs is computed using a second PSPM for modelling a second perception slice of the runtime stack, the first PSPM learned from data of a first sensor modality of the perception slice and the time series, and the second PSPM learned independently thereof from data of a second sensor modality of the second perception slice and the second time series.

Performance testing for robotic systems

A method of annotating known objects in road images captured from a sensor-equipped vehicle, the method implemented in an annotation system and comprising: receiving at the annotation system a road image containing a view of a known object; receiving ego localization data, as computed in a map frame of reference, via localization applied to sensor data captured by the sensor-equipped vehicle, the ego localization data indicating an image capture pose of the road image in the map frame of reference; determining, from a predetermined road map, an object location of the known object in the map frame of reference, the predetermined road map representing a road layout the map frame of reference, wherein the known object is one of: a piece of road structure, and an object on or adjacent a road; computing, in an image plane defined by the image capture pose, an object projection, by projecting an object model of the known object from the object location into the image plane; and storing, in an image database, image data of the road image, in association with annotation data of the object projection for annotating the image data with a location of the known object in the image plane.

Image annotation tools

A perception model is trained to classify inputs in relation to a discrete set of leaf node classes. A hierarchical classification tree encodes hierarchical relationships between the leaf node classes. A training loss function is dependent on a classification score for a given training input a its ground truth leaf node class of the training input, but also classification scores for at least some others of the leaf node classes, with the classification scores of the other leaf node classes weighted in dependence on their hierarchical relationship to the ground truth leaf node class within the hierarchical classification tree.

Structure detection models

In one aspect, hierarchical image segmentation is applied to an image formed of a plurality of pixels, by classifying the pixels according to a hierarchical classification scheme, in which at least some of those pixels are classified by a parent level classifier in relation to a set of parent classes, each of which is associated with a subset of child classes, and each of those pixels is also classified by at least one child level classifier in relation to one of the subsets of child classes, wherein each of the parent classes corresponds to a category of visible structure, and each of the subset of child classes associated with it corresponds to a different type of visible structure within that category.

Image segmentation

Techniques are described for determining the value of individual facts in a knowledge base, and various applications of such fact values. In one example, the knowledge base is part of a question answering system. A ranking of knowledge base facts based on the number of times each of the knowledge base facts is used in answering user questions (e.g., as determined from question answering logs) is used to derive a fact value function that may then be used to determine the value of other facts included in or subsequently added to the knowledge base.

Determining the value of facts in a knowledge base and related techniques

A query parsing system uses a multi-stage process to parse the text of incoming queries before attempting to answer the queries. The multi-stage configuration involves a first trained classifier to determine the query type, or intent, of the text and a plurality of second trained classifiers, where each of the second trained classifiers is configured particularly for one specific respective query type. During query processing, the first trained classifier is used on the text to identify the query type. A second trained classifier for that specific identified query type is then found and used on the text to identify what strings in the text correspond to specific entities needed to resolve the query. The identified text strings and query type are then placed into a form understandable by a knowledge base and sent to the knowledge base for resolution. The classifiers may be trained using queries and answers previously processed by the knowledge base using a rules/template resolution process.

Multi-stage query processing

Techniques are described for adding knowledge to a knowledge base.

Adding to a knowledge base using an ontological analysis of unstructured text

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Cambridge, United Kingdom

Sina Samangooei