US 12,469,256 B2
Performance of complex optimization tasks with improved efficiency via neural meta-optimization of experts
Avneesh Sud, Belmont, CA (US); Andrea Tagliasacchi, Toronto (CA); and Ben Usman, Boston, MA (US)
Assigned to GOOGLE LLC, Mountain View, CA (US)
Filed by Google LLC, Mountain View, CA (US)
Filed on Jul. 21, 2022, as Appl. No. 17/870,462.
Claims priority of provisional application 63/224,079, filed on Jul. 21, 2021.
Prior Publication US 2023/0040793 A1, Feb. 9, 2023
Int. Cl. G06V 10/77 (2022.01); G06N 5/022 (2023.01); G06T 7/70 (2017.01)
CPC G06V 10/7715 (2022.01) [G06N 5/022 (2013.01); G06T 7/70 (2017.01); G06T 2207/20084 (2013.01); G06V 2201/07 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A computer-implemented method for performing complex optimization tasks with improved efficiency or accuracy, the method comprising:
obtaining, by a computing system comprising one or more computing devices, a set of input data, wherein the input data comprises a plurality of images that depict a scene;
processing, by the computing system, the input data with one or more existing expert models to generate one or more expert outputs, wherein the one or more expert outputs comprise a plurality of features detected in the plurality of images;
processing, by the computing system, the one or more expert outputs with a meta-optimization neural network to generate a predicted output;
performing, by the computing system, an optimization technique on the one or more expert outputs to generate an optimized output, wherein performing, by the computing system, the optimization technique on the one or more expert outputs to generate the optimized output comprises performing, by the computing system, a bundle adjustment technique on the plurality of features to generate the optimized output, and wherein the optimized output comprises a geometry of the plurality of images relative to the scene; and
modifying, by the computing system, one or more learnable parameters of the meta-optimization neural network based at least in part on a loss function that compares the predicted output with the optimized output.