公开(公告)号：US20230090941A1

公开(公告)日：2023-03-23

申请号：US17933840

申请日：2022-09-20

Applicant: QUALCOMM Incorporated

Inventor： Yawei LI ,  Bert MOONS ,  Tijmen Pieter Frederik BLANKEVOORT ,  Amirhossein HABIBIAN ,  Babak EHTESHAMI BEJNORDI

IPC: G06V20/40 ,  G06V10/774 ,  G06V10/77 ,  G06V10/82

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for processing a video stream using a machine learning model. An example method generally includes generating a first group of tokens from a first frame of the video stream and a second group of tokens from a second frame of the video stream. A first set of tokens associated with features to be reused from the first frame and a second set of tokens associated with features to be computed from the second frame are identified based on a comparison of tokens from the first group of tokens to corresponding tokens in the second group of tokens. A feature output is generated for portions of the second frame corresponding to the second set of tokens. Features associated with the first set of tokens are combined with the generated feature output into a representation of the second frame.

公开(公告)号：US20220156508A1

公开(公告)日：2022-05-19

申请号：US17455201

申请日：2021-11-16

Applicant: QUALCOMM Incorporated

Inventor： Bert MOONS ,  Parham NOORZAD ,  Andrii SKLIAR ,  Christopher LOTT ,  Tijmen Pieter Frederik BLANKEVOORT

IPC: G06K9/62 ,  G06N3/08

Abstract: Various aspects provide methods for a computing device selecting a neural network for a hardware configuration including using an accuracy predictor to select from a search space a neural network including a first plurality of the blockwise knowledge distillation trained search blocks, in which the accuracy predictor is built using search space trained blockwise knowledge distillation search blocks. Aspects may include selecting a second plurality of the blockwise knowledge distillation trained search blocks based on criteria of predicted accuracy using the accuracy predictor for the second plurality of the blockwise knowledge distillation trained search blocks. Aspects may include selecting the neural network based on a search of the blockwise knowledge distillation trained search blocks, initializing the blockwise knowledge distillation trained search blocks of the neural network using weights of the blockwise knowledge distillation trained search blocks, and fine-tuning the neural network using knowledge distillation, to generate a distilled neural network.