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：US20240104367A1

公开(公告)日：2024-03-28

申请号：US17934098

申请日：2022-09-21

Applicant: QUALCOMM Incorporated

Inventor： Jamie Menjay LIN ,  Debasmit DAS

IPC: G06N3/08 ,  H04B17/391

CPC classification number: G06N3/08 ,  H04B17/3913

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for training a machine learning model. An example method generally includes partitioning a machine learning model into a plurality of partitions. A request to update a respective partition of the plurality of partitions in the machine learning model is transmitted to each respective participating device of a plurality of participating devices in a federated learning scheme, and the request may specify that the respective partition is to be updated based on unique data at the respective participating device. Updates to one or more partitions in the machine learning model are received from the plurality of participating devices, and the machine learning model is updated based on the received updates.

公开(公告)号：US20230376753A1

公开(公告)日：2023-11-23

申请号：US18157723

申请日：2023-01-20

Applicant: QUALCOMM Incorporated

Inventor： Seokeon CHOI ,  Sungha CHOI ,  Seunghan YANG ,  Hyunsin PARK ,  Debasmit DAS ,  Sungrack YUN

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Systems and techniques are provided for training a neural network model or machine learning model. For example, a method of augmenting training data can include augmenting, based on a randomly initialized neural network, training data to generate augmented training data and aggregating data with a plurality of styles from the augmented training data to generate aggregated training data. The method can further include applying semantic-aware style fusion to the aggregated training data to generate fused training data and adding the fused training data as fictitious samples to the training data to generate updated training data for training the neural network model or machine learning model.

公开(公告)号：US20230297653A1

公开(公告)日：2023-09-21

申请号：US17655506

申请日：2022-03-18

Applicant: QUALCOMM Incorporated

Inventor： Debasmit DAS ,  Sungrack YUN ,  Fatih Murat PORIKLI

IPC: G06F21/32 ,  G06N5/02

CPC classification number: G06F21/32 ,  G06N5/022

Abstract: Certain aspects of the present disclosure provide techniques for improved domain adaptation in machine learning. A feature tensor is generated by processing input data using a feature extractor. A first set of logits is generated by processing the feature tensor using a domain-agnostic classifier, and a second set of logits is generated by processing the feature tensor using a domain-specific classifier. A loss is computed based at least in part on the first set of logits and the second set of logits, where the loss includes a divergence loss component. The feature extractor, the domain-agnostic classifier, and the domain-specific classifier are refined using the loss.

公开(公告)号：US20220284290A1

公开(公告)日：2022-09-08

申请号：US17653855

申请日：2022-03-07

Applicant: QUALCOMM Incorporated

Inventor： Debasmit DAS ,  Yash Sanjay BHALGAT ,  Fatih Murat PORIKLI

IPC: G06N3/08

Abstract: Certain aspects of the present disclosure provide techniques for provide a method, comprising: receiving input data for a layer of a neural network model; selecting a target code for the input data; and determining weights for the layer based on an autoencoder loss and the target code.

公开(公告)号：US20250166236A1

公开(公告)日：2025-05-22

申请号：US18511692

申请日：2023-11-16

Applicant: QUALCOMM Incorporated

Inventor： Kambiz AZARIAN YAZDI ,  Fatih Murat PORIKLI ,  Qiqi HOU ,  Debasmit DAS

IPC: G06T11/00 ,  G06F40/284 ,  G06T5/00

Abstract: Certain aspects of the present disclosure provide techniques for generating an output image based on a text prompt. A method may include receiving the text prompt; providing a user interface comprising one or more input elements associated with one or more words of the text prompt; receiving input corresponding to at least one of the one or more input elements, the input indicating a semantic importance for each of at least one of the one or more words associated with the at least one of the one or more input elements; and generating the output image based on the text prompt and the input.

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.

公开(公告)号：US20240078800A1

公开(公告)日：2024-03-07

申请号：US17939361

申请日：2022-09-07

Applicant: QUALCOMM Incorporated

Inventor： Saeed VAHIDIAN ,  Manoj BHAT ,  Debasmit DAS ,  Shizhong Steve HAN ,  Fatih Murat PORIKLI

IPC: G06V10/82 ,  G06N3/08 ,  G06V10/764 ,  G06V10/774

CPC classification number: G06V10/82 ,  G06N3/08 ,  G06V10/764 ,  G06V10/774

Abstract: A method receives first and second data generated from a first and second domains including first and second set of objects, receiving first class labels for each of the first set of objects, and receiving second class labels for each of the second set of objects. The method generates a training dataset by augmenting the first data and corresponding first class labels, and locally updating neural network parameters of a model based on the training dataset. The method generates a validation dataset by augmenting the second data and corresponding second class labels, and globally updating the neural network parameters of the model based on the validation dataset. The method also generates multiple target labels for target data generated from a target domain including a third set of objects after globally updating the neural network parameters of the model based on the validation dataset.

公开(公告)号：US20240078797A1

公开(公告)日：2024-03-07

申请号：US18364728

申请日：2023-08-03

Applicant: QUALCOMM Incorporated

Inventor： Kambiz AZARIAN YAZDI ,  Debasmit DAS ,  Hyojin PARK ,  Fatih Murat PORIKLI

IPC: G06V10/778 ,  G06N3/0895 ,  G06V10/26 ,  G06V10/82

CPC classification number: G06V10/778 ,  G06N3/0895 ,  G06V10/267 ,  G06V10/82

Abstract: Techniques and systems are provided for performing online adaptation of machine learning model(s). For example, a process may include obtaining features extracted from a image by a machine learning model during inference and determining, by the machine learning model based on the features during inference, a plurality of keypoint estimates in the image and/or a bounding region estimate associated with an object in the image. The process may further include generating pseudo-label(s) based on the plurality of keypoint estimates and/or the bounding region estimate. The process may include determining at least one self-supervised loss based on the plurality of keypoint estimates and/or the bounding region estimate. The process may further include adapting, based on the at least one self-supervised loss, parameter(s) of the machine learning model. The process may include generating, using the machine learning model with the adapted parameter(s), a segmentation mask for the image (or another image).

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.