公开(公告)号：US20230154005A1

公开(公告)日：2023-05-18

申请号：US17807614

申请日：2022-06-17

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Hyojin PARK ,  Hong CAI ,  Debasmit DAS ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC: G06T7/10 ,  G06N3/08

CPC classification number: G06T7/10 ,  G06N3/08 ,  G06T2207/20084 ,  G06T2207/20081

Abstract: Aspects of the present disclosure relate to a novel framework for integrating both semantic and instance contexts for panoptic segmentation. In one example aspect, a method for processing image data includes: processing semantic feature data and instance feature data with a panoptic encoding generator to generate a panoptic encoding; processing the panoptic encoding to generate a panoptic segmentation features; and generating the panoptic segmentation mask based on the panoptic segmentation features.

公开(公告)号：US20230005165A1

公开(公告)日：2023-01-05

申请号：US17808520

申请日：2022-06-23

Applicant: QUALCOMM Incorporated

Inventor： Hong CAI ,  Janarbek MATAI ,  Shubhankar Mangesh BORSE ,  Yizhe ZHANG ,  Amin ANSARI ,  Fatih Murat PORIKLI

IPC: G06T7/50 ,  G06N3/08 ,  G06N3/04

Abstract: Certain aspects of the present disclosure provide techniques for cross-task distillation. A depth map is generated by processing an input image using a first machine learning model, and a segmentation map is generated by processing the depth map using a second machine learning model. A segmentation loss is computed based on the segmentation map and a ground-truth segmentation map, and the first machine learning model is refined based on the segmentation loss.

公开(公告)号：US20240273742A1

公开(公告)日：2024-08-15

申请号：US18165163

申请日：2023-02-06

Applicant: QUALCOMM Incorporated

Inventor： Debasmit DAS ,  Varun RAVI KUMAR ,  Shubhankar Mangesh BORSE ,  Senthil Kumar YOGAMANI

IPC: G06T7/50 ,  G06T7/10 ,  G06V10/26 ,  G06V10/70 ,  G06V10/764 ,  G06V10/82

CPC classification number: G06T7/50 ,  G06T7/10 ,  G06V10/26 ,  G06V10/764 ,  G06V10/768 ,  G06V10/82 ,  G06T2207/20021 ,  G06T2207/20072 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Disclosed are systems, apparatuses, processes, and computer-readable media for processing image data. For example, a process can include obtaining segmentation information associated with an image of a scene, the image including a plurality of pixels having a resolution, and obtaining depth information associated with one or more objects in the scene. A plurality of features can be generated corresponding to the plurality of pixels, wherein each feature of the plurality of features corresponds to a particular pixel of the plurality of pixels, and wherein each feature includes respective segmentation information of the particular pixel and respective depth information of the particular pixel. The plurality of features can be processed to generate a dense depth output corresponding to the image.

公开(公告)号：US20240249530A1

公开(公告)日：2024-07-25

申请号：US18157034

申请日：2023-01-19

Applicant: QUALCOMM Incorporated

Inventor： Varun RAVI KUMAR ,  Senthil Kumar YOGAMANI ,  Shubhankar Mangesh BORSE

IPC: G06V20/58 ,  G06V10/80

CPC classification number: G06V20/58 ,  G06V10/80 ,  B60W30/095

Abstract: Techniques and systems are provided for processing sensor data. For instance a process can include obtaining first sensor data of an environment, wherein the first sensor data includes a representation of a first object occluding a second object, obtaining second sensor data of the environment, wherein the second sensor data includes points associated with the first object and points associated with the second object, generating estimated segment data from the first sensor data, wherein the estimated segment data includes a first segment corresponding to the first object; matching points associated with the first object to the first segment, and deemphasizing points associated with the second object based on matching the points associated with the first object to the first segment.

公开(公告)号：US20240161368A1

公开(公告)日：2024-05-16

申请号：US18460903

申请日：2023-09-05

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Debasmit DAS ,  Hyojin PARK ,  Hong CAI ,  Risheek GARREPALLI ,  Fatih Murat PORIKLI

IPC: G06T11/60 ,  G06T3/40 ,  G06T7/12 ,  G06T7/50 ,  G06V10/44

CPC classification number: G06T11/60 ,  G06T3/40 ,  G06T7/12 ,  G06T7/50 ,  G06V10/44

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for regenerative learning to enhance dense predictions. In one example method, an input image is accessed. A dense prediction output is generated based on the input image using a dense prediction machine learning (ML) model, and a regenerated version of the input image is generated. A first loss is generated based on the input image and a corresponding ground truth dense prediction, and a second loss is generated based on the regenerated version of the input image. One or more parameters of the dense prediction ML model are updated based on the first and second losses.

公开(公告)号：US20240404003A1

公开(公告)日：2024-12-05

申请号：US18326437

申请日：2023-05-31

Applicant: QUALCOMM Incorporated

Inventor： Debasmit DAS ,  Hyojin PARK ,  Shubhankar Mangesh BORSE ,  Yu FU ,  Oleksandr BAILO ,  Mohsen GHAFOORIAN ,  Fatih Murat PORIKLI

IPC: G06T3/40

Abstract: Certain aspects of the present disclosure provide techniques for training and using an instance segmentation neural network to detect instances of a target object in an image. An example method generally includes generating, through an instance segmentation neural network, a first mask output from a first mask generation branch of the network. The method further includes generating, through the instance segmentation neural network, a second mask output from a second, parallel, mask generation branch of the network. The second mask output is typically of a lower resolution than the first mask output. The method further includes combining the first mask output and second mask output to generate a combined mask output. Based on the combined mask output, an output of the instance segmentation neural network is generated. One or more actions are taken based on the generated output.

公开(公告)号：US20240169542A1

公开(公告)日：2024-05-23

申请号：US18346470

申请日：2023-07-03

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Hyojin PARK ,  Risheek GARREPALLI ,  Debasmit DAS ,  Hong CAI ,  Fatih Murat PORIKLI

IPC: G06T7/10 ,  G06T5/20 ,  G06T5/50 ,  G06V10/44 ,  G06V10/80

CPC classification number: G06T7/10 ,  G06T5/20 ,  G06T5/50 ,  G06V10/44 ,  G06V10/806 ,  G06T2207/20221

Abstract: Techniques and systems are provided for generating one or more segmentations masks. For instance, a process may include generating a delta image based on a difference between a current image and a prior image. The process may further include processing, using a transform operation, the delta image and features representing the prior image to generate a transformed feature representation of the prior image. The process may include combining the transformed feature representation of the prior image with features representing the current image to generate a combined feature representation of the current image. The process may further include generating, based on the combined feature representation of the current image, a segmentation mask for the current image.

公开(公告)号：US20240078679A1

公开(公告)日：2024-03-07

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

CPC classification number: G06T7/11 ,  G06T7/74 ,  G06T2207/20112

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：US20220156528A1

公开(公告)日：2022-05-19

申请号：US17528141

申请日：2021-11-16

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Fatih Murat PORIKLI ,  Yizhe ZHANG ,  Ying WANG

IPC: G06K9/62 ,  G06N3/08

Abstract: A method applies a distance-based loss function to a boundary recognition model. The method classifies boundaries of an input with the boundary recognition model. The method also performs semantic segmentation based on the classifying of the boundaries, and outputting a segmentation map showing different classes of objects from the input, based on the semantic segmentation. The method may train an inverse transforming artificial neural network to predict a perspective transformation of an image so that the trained artificial neural network represents the distance-based loss function. The method may freeze weights of the inverse transforming artificial neural network, after training, to obtain the distance-based loss function. Training of the inverse transforming artificial neural network may include generating shifted, translated, and scaled versions of the image such that a ground truth comprises values corresponding to the amounts of shifting, translating, and scaling.

公开(公告)号：US20250148752A1

公开(公告)日：2025-05-08

申请号：US18502719

申请日：2023-11-06

Applicant: QUALCOMM Incorporated

Inventor： Vibashan VISHNUKUMAR SHARMINI ,  Shubhankar Mangesh BORSE ,  Hyojin PARK ,  Debasmit DAS ,  Munawar HAYAT ,  Fatih Murat PORIKLI

IPC: G06V10/75 ,  G06V30/148

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. In an example method, an input image is accessed, and the input image is processed using an image encoder to generate an image embedding tensor. The image embedding tensor is processed using a mask decoder machine learning model to generate a set of mask embedding tensors. A textual input is processed using a text encoder to generate a text embedding tensor. A set of augmented masks is generated based on aggregating the text embedding tensor with the set of mask embedding tensors.