US 12,112,482 B2
Techniques for interactive image segmentation networks
Ke Ding, San Jose, CA (US); Anthony Rhodes, Portland, OR (US); and Manan Goel, Portland, OR (US)
Assigned to Intel Corporation, Santa Clara, CA (US)
Filed by Intel Corporation, Santa Clara, CA (US)
Filed on Jan. 28, 2021, as Appl. No. 17/161,139.
Prior Publication US 2021/0225002 A1, Jul. 22, 2021
Int. Cl. G06T 7/11 (2017.01); G06N 3/045 (2023.01)
CPC G06T 7/11 (2017.01) [G06N 3/045 (2023.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 20 Claims
OG exemplary drawing
 
1. An apparatus comprising:
interface circuitry;
instructions; and
one or more first processor circuits to utilize the instructions to:
conduct a neural architecture search to select respective values for hyperparameters of a machine learning (ML) model, the ML model associated with a neural architecture having one or more encoders, one or more decoders, and one or more skip connections between the one or more encoders and the one or more decoders, the hyperparameters indicative of a first number of the one or more encoders and the one or more decoders to be included in the neural architecture, a second number of convolutional layers within respective ones of the one or more decoders, and a size of dilation in the respective ones of the one or more decoders; and
cause the interface circuitry to deploy the ML model with the respective values for the hyperparameters, the ML model to cause one or more second processor circuits to:
based on a first encoding frame with a first resolution, generate, with a first encoder, first output data including a second encoding frame with a second resolution; and
generate, with a first decoder, second output data including a first decoding frame with the first resolution based on a second decoding frame with the second resolution and a context frame with the second resolution.