公开(公告)号：US20250086946A1

公开(公告)日：2025-03-13

申请号：US18463756

申请日：2023-09-08

Applicant: QUALCOMM Incorporated

Inventor： Debasmit Das ,  Mohsen Ghafoorian ,  Oleksandr Bailo ,  Yu Fu ,  Hyojin Park ,  Shubhankar Mangesh Borse ,  Fatih Murat Porikli

IPC: G06V10/776 ,  G06T7/11 ,  G06T7/174 ,  G06T7/80 ,  G06V10/74 ,  G06V10/774

Abstract: A system stores first and second images generated by first and second cameras; applies a segmentation model to the first image to generate a first segmentation mask identifying object instances; applies the segmentation model to the second image to generate a second segmentation mask identifying the object instances; projects the first segmentation mask to a viewpoint of the second camera to generate a first projected segmentation mask; converts the first projected segmentation mask and the second segmentation mask to first and second semantic masks, respectively; and computes a first similarity value based on the first and second semantic masks. This may be repeated exchanging the first and second images to compute a second similarity value. The system determines a loss value based on the first similarity value and the second similarity value and trains the segmentation model based on the loss value.

公开(公告)号：US12210585B2

公开(公告)日：2025-01-28

申请号：US17198147

申请日：2021-03-10

Applicant: QUALCOMM Incorporated

Inventor： Yizhe Zhang ,  Shubhankar Mangesh Borse ,  Fatih Murat Porikli

IPC: G06N3/04 ,  G06F18/214 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084

Abstract: A method for processing a video includes receiving a video as an input at a first layer of an artificial neural network (ANN). A first frame of the video is processed to generate a first label. Thereafter, the artificial neural network is updated based on the first label. The updating is performed while concurrently processing a second frame of the video. In doing so, the temporal inconsistency between labels is reduced.

公开(公告)号：US12293521B2

公开(公告)日：2025-05-06

申请号：US17901429

申请日：2022-09-01

Applicant: QUALCOMM Incorporated

Inventor： Chung-Chi Tsai ,  Shubhankar Mangesh Borse ,  Meng-Lin Wu ,  Venkata Ravi Kiran Dayana ,  Fatih Murat Porikli ,  An Chen

IPC: G06T7/11 ,  G06T7/73 ,  G06V30/19

Abstract: Methods, systems, and apparatuses for image segmentation are provided. For example, a computing device may obtain an image, and may apply a process to the image to generate input image feature data and input image segmentation data. Further, the computing device may obtain reference image feature data and reference image classification data for a plurality of reference images. The computing device may generate reference image segmentation data based on the reference image feature data, the reference image classification data, and the input image feature data. The computing device may further blend the input image segmentation data and the reference image segmentation data to generate blended image segmentation data. The computing device may store the blended image segmentation data within a data repository. In some examples, the computing device provides the blended image segmentation data for display.

公开(公告)号：US20240153249A1

公开(公告)日：2024-05-09

申请号：US18467455

申请日：2023-09-14

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh Borse ,  Marvin Richard Klingner ,  Varun Ravi Kumar ,  Senthil Kumar Yogamani ,  Fatih Murat Porikli

IPC: G06V10/774 ,  G06V10/26 ,  G06V10/40 ,  G06V10/80 ,  G06V20/56

CPC classification number: G06V10/774 ,  G06V10/26 ,  G06V10/40 ,  G06V10/803 ,  G06V20/56

Abstract: This disclosure provides systems, methods, and devices for image signal processing that support training object recognition models. In a first aspect, a method of image processing includes training a first modality imaging system; receiving time-synchronized first input data samples and second input data samples from the first modality imaging system and a second modality imaging system, respectively; processing the first input data samples in the first modality imaging system to generate first output; processing the second input data samples in the second modality imaging system to generate second output; and training the second modality imaging system based on the first output and the second output. Other aspects and features are also claimed and described.