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公开(公告)号：US11106944B2
公开(公告)日：2021-08-31
申请号：US16557330
申请日：2019-08-30
Applicant: Adobe Inc.
Inventor： Viswanathan Swaminathan , Saayan Mitra , Han Guo
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.
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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：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：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
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.
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