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公开(公告)号:US20240070812A1
公开(公告)日:2024-02-29
申请号:US18358857
申请日:2023-07-25
Applicant: QUALCOMM Incorporated
Inventor: Risheek GARREPALLI , Rajeswaran CHOCKALINGAPURAMRAVINDRAN , Jisoo JEONG , Fatih Murat PORIKLI
CPC classification number: G06T3/4053 , G06T7/579
Abstract: A processor-implemented method comprises processing a single level cost volume across multiple processing stages by varying a receptive field across each of the processing stages. The method also includes performing a learning-based correspondence estimation task based on the processing. The varying may include processing a different resolution of the cost volume at each processing stage while maintaining a same neighborhood sampling radius. The resolution may increase from a first processing stage to a later processing stage. The varying may also include varying a neighborhood sampling radius at each of the processing stages while maintaining a same resolution. The task may be optical flow estimation or stereo estimation.
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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
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.
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公开(公告)号:US20250124301A1
公开(公告)日:2025-04-17
申请号:US18488779
申请日:2023-10-17
Applicant: QUALCOMM Incorporated
Inventor: Amirhossein HABIBIAN , Risheek GARREPALLI , Fatih Murat PORIKLI
IPC: G06N3/096 , G06N3/0455 , G06N3/0464 , G06N3/0475 , G06T11/00
Abstract: Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. During a first iteration of processing data using a denoising backbone of a diffusion machine learning model, a first latent tensor is generated using a lower resolution block of the denoising backbone, and a first feature tensor is generated based on processing the first latent tensor using a higher resolution block of the denoising backbone, the higher resolution block using a higher resolution than the lower resolution block. A second latent tensor is generated based on processing the first latent tensor using an adapter block of the denoising backbone. During a second iteration of processing the data using the denoising backbone, a second feature tensor is generated based on processing the second latent tensor using the higher resolution block.
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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.
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5.
公开(公告)号:US20250148633A1
公开(公告)日:2025-05-08
申请号:US18666502
申请日:2024-05-16
Applicant: QUALCOMM Incorporated
Inventor: Rajeev YASARLA , Hong CAI , Risheek GARREPALLI , Yinhao ZHU , Jisoo JEONG , Yunxiao SHI , Manish Kumar SINGH , Fatih Murat PORIKLI
Abstract: Systems and techniques are provided for generating depth information. For example, a process can include obtaining a first feature volume including visual features corresponding to each respective frame included in a first set of frames. A first query generator network can generate reconstruction features associated with a reconstructed feature volume corresponding to the first feature volume. Based on the first feature volume, a second query generator network can generate motion features associated with predicted future motion corresponding to the first feature volume. An initial depth prediction can be generated for each respective frame based on cross-attention between features of a depth prediction decoder, the reconstruction features, and the motion features. A refined depth prediction can be generated for each respective based on cross-attention between the initial depth prediction, the reconstruction features, and the motion features.
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公开(公告)号:US20240303841A1
公开(公告)日:2024-09-12
申请号:US18538869
申请日:2023-12-13
Applicant: QUALCOMM Incorporated
Inventor: Rajeev YASARLA , Hong CAI , Jisoo JEONG , Risheek GARREPALLI , Yunxiao SHI , Fatih Murat PORIKLI
Abstract: Disclosed are systems and techniques for capturing images (e.g., using a monocular image sensor) and detecting depth information. According to some aspects, a computing system or device can generate a feature representation of a current image and update accumulated feature information for storage in a memory based on a feature representation of a previous image and optical flow information of the previous image. The accumulated feature information can include accumulated image feature information associated with a plurality of previous images and accumulated optical flow information associated of the plurality of previous images. The computing system or device can obtain information associated with relative motion of the current image based on the accumulated feature information and the feature representation of the current image. The computing system or device can estimate depth information for the current image based on the information associated with the relative motion and the accumulated feature information.
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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
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.
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公开(公告)号:US20250139733A1
公开(公告)日:2025-05-01
申请号:US18499604
申请日:2023-11-01
Applicant: QUALCOMM Incorporated
Inventor: Jisoo JEONG , Hong CAI , Risheek GARREPALLI , Jamie Menjay LIN , Munawar HAYAT , Fatih Murat PORIKLI
Abstract: Systems and techniques described herein relate to generating an inter-frame from a first and second frame. An apparatus includes a memory storing a first frame and a second frame; and a processor coupled to the memory and configured to: estimate at least one optical flow between the first frame and the second frame; generate, based on the at least one optical flow, at least one occlusion mask; generate, based on the at least one optical flow and the at least one occlusion mask, at least one weighting mask; generate, based on the at least one optical flow and the at least one weighting mask, at least one inter-frame optical flow; generate, based on the at least one inter-frame optical flow and at least one of the first frame or the second frame, at least one warped frame; and generate, based on the at least one warped frame, an inter-frame.
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公开(公告)号:US20250095182A1
公开(公告)日:2025-03-20
申请号:US18468656
申请日:2023-09-15
Applicant: QUALCOMM Incorporated
Inventor: Jisoo JEONG , Hong CAI , Babak EHTESHAMI BEJNORDI , Risheek GARREPALLI , Rajeev YASARLA , Fatih Murat PORIKLI
Abstract: Techniques and systems are provided for image processing. For instance, a process can include correlating a first set of features from a first viewpoint with a second set of features from a second viewpoint at a first time period to generate a first disparity cost volume; correlating a third set of features from the first viewpoint at a second time period with the first set of features to generate a first optical flow cost volume; gating the first disparity cost volume to generate first intermediate disparity information; gating the first optical flow cost volume to generate first intermediate optical flow information; correlating the first set of features, the second set of features, and the first intermediate optical flow information to generate disparity information for output; and correlating the third set of features, the first set of features, and the first intermediate disparity information to generate optical flow information for output.
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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
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.
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