公开(公告)号：US12210976B2

公开(公告)日：2025-01-28

申请号：US17219339

申请日：2021-03-31

Applicant: Salesforce.com, Inc.

Inventor： Hualin Liu ,  Chu Hong Hoi ,  Junnan Li

IPC: G06N3/084 ,  G06F18/214 ,  G06F18/22 ,  G06N3/088 ,  G06V10/75

Abstract: Embodiments described herein provide systems and methods for learning representation from unlabeled videos. Specifically, a method may comprise generating a set of strongly-augmented samples and a set of weakly-augmented samples from the unlabeled video samples; generating a set of predictive logits by inputting the set of strongly-augmented samples into a student model and a first teacher model; generating a set of artificial labels by inputting the set of weakly-augmented samples to a second teacher model that operates in parallel to the first teacher model, wherein the second teacher model shares one or more model parameters with the first teacher model; computing a loss objective based on the set of predictive logits and the set of artificial labels; updating student model parameters based on the loss objective via backpropagation; and updating the shared parameters for the first teacher model and the second teacher model based on the updated student model parameters.

公开(公告)号：US20220156593A1

公开(公告)日：2022-05-19

申请号：US17219339

申请日：2021-03-31

Applicant: salesforce.com, inc.

Inventor： Hualin Liu ,  Chu Hong Hoi ,  Junnan Li

IPC: G06N3/08 ,  G06K9/62

Abstract: Embodiments described herein provide systems and methods for learning representation from unlabeled videos. Specifically, a method may comprise generating a set of strongly-augmented samples and a set of weakly-augmented samples from the unlabeled video samples; generating a set of predictive logits by inputting the set of strongly-augmented samples into a student model and a first teacher model; generating a set of artificial labels by inputting the set of weakly-augmented samples to a second teacher model that operates in parallel to the first teacher model, wherein the second teacher model shares one or more model parameters with the first teacher model; computing a loss objective based on the set of predictive logits and the set of artificial labels; updating student model parameters based on the loss objective via backpropagation; and updating the shared parameters for the first teacher model and the second teacher model based on the updated student model parameters.