公开(公告)号：US20230139824A1

公开(公告)日：2023-05-04

申请号：US17519311

申请日：2021-11-04

Applicant: ADOBE INC.

Inventor： Trisha Mittal ,  Viswanathan Swaminathan ,  Ritwik Sinha ,  Saayan Mitra ,  David Arbour ,  Somdeb Sarkhel

IPC: G06Q30/02 ,  G06N20/00

Abstract: Various disclosed embodiments are directed to using one or more algorithms or models to select a suitable or optimal variation, among multiple variations, of a given content item based on feedback. Such feedback guides the algorithm or model to arrive at suitable variation result such that the variation result is produced as the output for consumption by users. Further, various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things, as described in more detail below.

公开(公告)号：US11430219B2

公开(公告)日：2022-08-30

申请号：US16952675

申请日：2020-11-19

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Viswanathan Swaminathan ,  Stefano Petrangeli ,  Md Maminur Islam

IPC: G06V20/40 ,  G06N3/08 ,  G06K9/62

Abstract: Systems and methods predict a performance metric for a video and identify key portions of the video that contribute to the performance metric, which can be used to edit the video to improve the ultimate viewer response to the video. An initial performance metric is computed for an initial video (e.g., using a neural network). A perturbed video is generated by perturbing a video portion of the initial video. A modified performance metric is computed for the perturbed video. Based on a difference between the initial and modified performance metrics, the system determines that the video portion contributed to a predicted user viewer response to the initial video. An indication of the video portion that contributed to the predicted user viewer response is provided as output, which can be used to edit the video to improve the predicted viewer response.

公开(公告)号：US20220156499A1

公开(公告)日：2022-05-19

申请号：US16952675

申请日：2020-11-19

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Viswanathan Swaminathan ,  Stefano Petrangeli ,  Md Maminur Islam

IPC: G06K9/00 ,  G06N3/08 ,  G06K9/62

Abstract: Systems and methods predict a performance metric for a video and identify key portions of the video that contribute to the performance metric, which can be used to edit the video to improve the ultimate viewer response to the video. An initial performance metric is computed for an initial video (e.g., using a neural network). A perturbed video is generated by perturbing a video portion of the initial video. A modified performance metric is computed for the perturbed video. Based on a difference between the initial and modified performance metrics, the system determines that the video portion contributed to a predicted user viewer response to the initial video. An indication of the video portion that contributed to the predicted user viewer response is provided as output, which can be used to edit the video to improve the predicted viewer response.

公开(公告)号：US11308523B2

公开(公告)日：2022-04-19

申请号：US15457882

申请日：2017-03-13

Applicant: Adobe Inc.

Inventor： Wreetabrata Kar ,  Viswanathan Swaminathan ,  Sarathkrishna Swaminathan

IPC: G06Q30/00 ,  G06Q30/02 ,  H04N21/258 ,  G06F16/435 ,  G06F16/635 ,  H04N21/81

Abstract: This disclosure generally covers systems and methods that determine demographic labels for a user or a group of users by using digital inputs within a predictive model for demographic classification. In particular, the disclosed systems and methods use a unique combination of classification algorithms to determine demographic labels for users as a potential audience of digital content items. When applying the combination of classification algorithms, the disclosed systems and methods use a first classification algorithm to determine user-level-latent features for each user within a group of users based on demographic-label statistics associated with particular digital content items. The disclosed systems and methods then use the user-level-latent features and session-level features (from sessions of each user consuming the digital content items) as inputs in a second classification algorithm to determine a demographic label for each user within the group of users.

公开(公告)号：US11252393B2

公开(公告)日：2022-02-15

申请号：US17074189

申请日：2020-10-19

Applicant: Adobe Inc.

Inventor： Stefano Petrangeli ,  Viswanathan Swaminathan ,  Gwendal Brieuc Christian Simon

IPC: H04N13/194 ,  H04N13/376

Abstract: In implementations of trajectory-based viewport prediction for 360-degree videos, a video system obtains trajectories of angles of users who have previously viewed a 360-degree video. The angles are used to determine viewports of the 360-degree video, and may include trajectories for a yaw angle, a pitch angle, and a roll angle of a user recorded as the user views the 360-degree video. The video system clusters the trajectories of angles into trajectory clusters, and for each trajectory cluster determines a trend trajectory. When a new user views the 360-degree video, the video system compares trajectories of angles of the new user to the trend trajectories, and selects trend trajectories for a yaw angle, a pitch angle, and a roll angle for the user. Using the selected trend trajectories, the video system predicts viewports of the 360-degree video for the user for future times.

公开(公告)号：US20220027722A1

公开(公告)日：2022-01-27

申请号：US16939661

申请日：2020-07-27

Applicant: Adobe Inc.

Inventor： Gang Wu ,  Viswanathan Swaminathan ,  Ryan Rossi ,  Hongchang Gao

IPC: G06N3/08 ,  G06N3/04

Abstract: A deep relational factorization machine (“DRFM”) system is configured to provide a high-order prediction based on high-order feature interaction data for a dataset of sample nodes. The DRFM system can be configured with improved factorization machine (“FM”) techniques for determining high-order feature interaction data describing interactions among three or more features. The DRFM system can be configured with improved graph convolutional neural network (“GCN”) techniques for determining sample interaction data describing sample interactions among sample nodes, including sample interaction data that is based on the high-order feature interaction data. The DRFM system generates a high-order prediction based on the high-order feature interaction embedding vector and the sample interaction embedding vector. The high-order prediction can be provided to a prediction computing system configured to perform operations based on the high-order prediction.

公开(公告)号：US11217208B2

公开(公告)日：2022-01-04

申请号：US16834776

申请日：2020-03-30

Applicant: Adobe Inc.

Inventor： Stefano Petrangeli ,  Viswanathan Swaminathan ,  Na Wang ,  Haoliang Wang ,  Gwendal Simon

IPC: G09G5/36 ,  G06F16/487 ,  G06F16/44

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that iteratively select versions of augmented reality objects at augmented reality levels of detail to provide for download to a client device to reduce start-up latency associated with providing a requested augmented reality scene. In particular, in one or more embodiments, the disclosed systems determine utility and priority metrics associated with versions of augmented reality objects associated with a requested augmented reality scene. The disclosed systems utilize the determined metrics to select versions of augmented reality objects that are likely to be viewed by the client device and improve the quality of the augmented reality scene as the client device moves through the augmented reality scene. In at least one embodiment, the disclosed systems iteratively select versions of augmented reality objects at various levels of detail until the augmented reality scene is fully downloaded.

公开(公告)号：US20210337222A1

公开(公告)日：2021-10-28

申请号：US16860758

申请日：2020-04-28

Applicant: Adobe Inc.

Inventor： Viswanathan Swaminathan ,  Stefano Petrangeli ,  Gwendal Simon

IPC: H04N19/192 ,  H04N19/176 ,  H04N19/186

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media to enhance texture image delivery and processing at a client device. For example, the disclosed systems can utilize a server-side compression combination that includes, in sequential order, a first compression pass, a decompression pass, and a second compression pass. By applying this compression combination to a texture image at the server-side, the disclosed systems can leverage both GPU-friendly and network-friendly image formats. For example, at a client device, the disclosed system can instruct the client device to execute a combination of decompression-compression passes on a GPU-network-friendly image delivered over a network connection to the client device. In so doing, client device can generate a tri-pass-compressed-texture from a decompressed image comprising texels with color palettes based on previously reduced color palettes from the first compression pass at the server-side, which reduces computational overhead and increases performance speed.

公开(公告)号：US11086843B2

公开(公告)日：2021-08-10

申请号：US15788481

申请日：2017-10-19

Applicant: ADOBE INC.

Inventor： Viswanathan Swaminathan ,  Saayan Mitra

IPC: G06F16/00 ,  G06F16/23 ,  G06F16/583 ,  H04N21/6336 ,  H03M7/30 ,  H04N21/21

Abstract: Embodiments of the present disclosure provide systems, methods, and computer storage media for optimizing computing resources generally associated with cloud-based media services. Instead of decoding digital assets on-premises to stream to a remote client device, an encoded asset can be streamed to the remote client device. A codebook employable for decoding the encoded asset can be embedded into the stream transmitted to the remote client device, so that the remote client device can extract the embedded codebook, and employ the extracted codebook to decode the encoded asset locally. In this way, not only are processing resources associated with on-premises decoding eliminated, but on-premises storage of codebooks can be significantly reduced, while expensive bandwidth is freed up by virtue of transmitting a smaller quantity of data from the cloud to the remote client device.

公开(公告)号：US20210064934A1

公开(公告)日：2021-03-04

申请号：US16557330

申请日：2019-08-30

Applicant: Adobe Inc.

Inventor： Viswanathan Swaminathan ,  Saayan Mitra ,  Han Guo

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00

Abstract: This disclosure relates to methods, non-transitory computer readable media, and systems that can initially train a machine-learning-logo classifier using synthetic training images and incrementally apply the machine-learning-logo classifier to identify logo images to replace the synthetic training images as training data. By incrementally applying the machine-learning-logo classifier to determine one or both of logo scores and positions for logos within candidate logo images, the disclosed systems can select logo images and corresponding annotations indicating positions for ground-truth logos. In some embodiments, the disclosed systems can further augment the iterative training of a machine-learning-logo classifier to include user curation and removal of incorrectly detected logos from candidate images, thereby avoiding the risk of model drift across training iterations.