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：US20240070541A1

公开(公告)日：2024-02-29

申请号：US18365664

申请日：2023-08-04

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Varun RAVI KUMAR ,  David UNGER ,  Senthil Kumar YOGAMANI ,  Fatih Murat PORIKLI

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Techniques and systems are provided for training a machine learning (ML) model. A technique can include generating a first set of features for objects in images, predicting image feature labels for the first set of features, comparing the predicted image feature labels to ground truth image feature labels to evaluate a first loss function, perform a perspective transform on the first set of features to generate a birds eye view (BEV) projected image features, combining the BEV projected image features and a first set of flattened features to generate combined image features, generating a segmented BEV map of the environment based on the combined image features, comparing the segmented BEV map to a ground truth segmented BEV map to evaluate a second loss function, and training the ML model for generation of segmented BEV maps based on the evaluated first loss function and the evaluated second loss function.

公开(公告)号：US20240020848A1

公开(公告)日：2024-01-18

申请号：US18349771

申请日：2023-07-10

Applicant: QUALCOMM Incorporated

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

IPC: G06T7/168

CPC classification number: G06T7/168 ,  G06T2207/20132

Abstract: Systems and techniques are provided for processing one or more images. For instance, according to some aspects of the disclosure, a method may include obtaining an unlabeled image and generating at least one transformed image based on the unlabeled image. The method may include processing the unlabeled image using a pre-trained semantic segmentation model to generate a first segmentation output. The method may further include processing the at least one transformed image using the pre-trained semantic segmentation model to generate at least a second segmentation output. The method may include fine-tuning, based on the first segmentation output and at least the second segmentation output, one or more parameters of the pre-trained semantic segmentation model.

公开(公告)号：US20230004812A1

公开(公告)日：2023-01-05

申请号：US17808949

申请日：2022-06-24

Applicant: QUALCOMM Incorporated

Inventor： Shubhankar Mangesh BORSE ,  Hong CAI ,  Yizhe ZHANG ,  Fatih Murat PORIKLI

IPC: G06N3/08 ,  G06K9/62

Abstract: Certain aspects of the present disclosure provide techniques for training neural networks using hierarchical supervision. An example method generally includes training a neural network with a plurality of stages using a training data set and an initial number of classification clusters into which data in the training data set can be classified. A cluster-validation set performance metric is generated for each stage based on a reduced number of classification clusters relative to the initial number of classification clusters and a validation data set. A number of classification clusters to implement at each stage is selected based on the cluster-validation set performance metric and an angle selected relative to the cluster-validation set performance metric for a last stage of the neural network. The neural network is retrained based on the training data set and the selected number of classification clusters for each stage, and the trained neural network is deployed.

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.