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公开(公告)号：US20240420389A1
公开(公告)日：2024-12-19
申请号：US18526855
申请日：2023-12-01
Applicant: ADOBE INC.
Inventor： Vineet Batra , Sumit Chaturvedi , Abhishek Rai , Pranav Vineet Aggarwal , Ajinkya Gorakhnath Kale , Aman Jeph , Ankit Phogat , Sumit Dhingra , Fengbin Chen , Kshitiz Garg , Milos Hasan , Midhun Harikumar , Gaurav Suresh Pathak , Souymodip Chakraborty
IPC: G06T11/20 , G06V10/764 , G06V10/774
Abstract: Systems and methods for generating tile-able patterns from text include obtaining a text prompt and generating, by a generation prior model, a latent vector based on the text prompt, where the generation prior model is trained to output vectors within a distribution of tile-able patterns. An image generation model then generates an output image based on the latent vector. The output image comprises a tile-able pattern including an element from the text prompt.
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公开(公告)号：US12050647B2
公开(公告)日：2024-07-30
申请号：US17877469
申请日：2022-07-29
Applicant: Adobe Inc.
Inventor： Somdeb Sarkhel , Xiang Chen , Viswanathan Swaminathan , Swapneel Mehta , Saayan Mitra , Ryan Rossi , Han Guo , Ali Aminian , Kshitiz Garg
IPC: G06F16/90 , G06F16/901 , G06N3/045 , G06Q50/00
CPC classification number: G06F16/9024 , G06N3/045 , G06Q50/01
Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.
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公开(公告)号：US12067499B2
公开(公告)日：2024-08-20
申请号：US17087116
申请日：2020-11-02
Applicant: Adobe Inc.
Inventor： Akhilesh Kumar , Xiaozhen Xue , Daniel Miranda , Nicolas Huynh Thien , Kshitiz Garg
Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.
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Patent Agency Ranking
公开(公告)号：US20240037149A1
公开(公告)日：2024-02-01
申请号：US17877469
申请日：2022-07-29
Applicant: Adobe Inc.
Inventor： Somdeb Sarkhel , Xiang Chen , Viswanathan Swaminathan , Swapneel Mehta , Saayan Mitra , Ryan Rossi , Han Guo , Ali Aminian , Kshitiz Garg
IPC: G06F16/901 , G06N3/04 , G06Q50/00
CPC classification number: G06F16/9024 , G06N3/0454 , G06Q50/01
Abstract: Techniques for recommending hashtags, including trending hashtags, are disclosed. An example method includes accessing a graph. The graph includes video nodes representing videos, historical hashtag nodes representing historical hashtags, and edges indicating associations among the video nodes and the historical hashtag nodes. A trending hashtag is identified. An edge is added to the graph between a historical hashtag node representing a historical hashtag and a trending hashtag node representing the trending hashtag, based on a semantic similarity between the historical hashtag and the trending hashtag. A new video node representing a new video is added to the video nodes of the graph. A graph neural network (GNN) is applied to the graph, and the GNN predicts a new edge between the trending hashtag node and the new video node. The trending hashtag is recommended for the new video based on prediction of the new edge.
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公开(公告)号：US12266181B2
公开(公告)日：2025-04-01
申请号：US17531568
申请日：2021-11-19
Applicant: Adobe Inc.
Inventor： Shivam Nalin Patel , Kshitiz Garg , Han Guo , Ali Aminian , Aashish Misraa
IPC: G06V20/40 , G06F18/214 , G06F18/23 , G06F18/25 , G06F40/205
Abstract: Embodiments are disclosed for receiving a user input and an input video comprising multiple frames. The method may include extracting a text feature from the user input. The method may further include extracting a plurality of image features from the frames. The method may further include identifying one or more keyframes from the frames that include the object. The method may further include clustering one or more groups of the one or more keyframes. The method may further include generating a plurality of segmentation masks for each group. The method may further include determining a set of reference masks corresponding to the user input and the object. The method may further include generating a set of fusion masks by combining the plurality of segmentation masks and the set of reference masks. The method may further include propagating the set of fusion masks and outputting a final set of masks.
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公开(公告)号：US20240362506A1
公开(公告)日：2024-10-31
申请号：US18771409
申请日：2024-07-12
Applicant: Adobe Inc.
Inventor： Akhilesh Kumar , Xiaozhen Xue , Daniel Miranda , Nicolas Huynh Thien , Kshitiz Garg
Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.
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公开(公告)号：US20230140369A1
公开(公告)日：2023-05-04
申请号：US17452626
申请日：2021-10-28
Applicant: ADOBE INC.
Inventor： Ali Aminian , William Lawrence Marino , Kshitiz Garg , Aseem Agarwala
IPC: G06F16/738 , G06K9/00 , G06F3/0482
Abstract: Embodiments of the present invention provide systems, methods, and computer storage media for extracting moments of interest (e.g., video frames, video segments) from a video. In an example embodiment, independent and/or orthogonal machine learning models are used to extract different types of features considering different modalities, and each frame in the video is assigned an importance score for each model. The importance scores for each model are combined into an aggregated importance score for each frame in the video. Depending on the embodiment, the aggregated importance scores are used to visualize the score per frame, identify moments of interest, automatically crop down the video into a highlight reel, browse or visualize the moments of interest within the video, and/or search across multiple videos.
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公开(公告)号：US20220138596A1
公开(公告)日：2022-05-05
申请号：US17087116
申请日：2020-11-02
Applicant: Adobe Inc.
Inventor： Akhilesh Kumar , Xiaozhen Xue , Daniel Miranda , Nicolas Huynh Thien , Kshitiz Garg
Abstract: This disclosure describes one or more implementations of a video inference system that utilizes machine-learning models to efficiently and flexibly process digital videos utilizing various improved video inference architectures. For example, the video inference system provides a framework for improving digital video processing by increasing the efficiency of both central processing units (CPUs) and graphics processing units (GPUs). In one example, the video inference system utilizes a first video inference architecture to reduce the number of computing resources needed to inference digital videos by analyzing multiple digital videos utilizing sets of CPU/GPU containers along with parallel pipeline processing. In a further example, the video inference system utilizes a second video inference architecture that facilitates multiple CPUs to preprocess multiple digital videos in parallel as well as a GPU to continuously, sequentially, and efficiently inference each of the digital videos.
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