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公开(公告)号：US20210319566A1
公开(公告)日：2021-10-14
申请号：US17350129
申请日：2021-06-17
Applicant: Adobe Inc.
Inventor： Xingyu Liu , Hailin Jin , Joonyoung Lee
Abstract: Technology is disclosed herein for learning motion in video. In an implementation, an artificial neural network extracts features from a video. A correspondence proposal (CP) module performs, for at least some of the features, a search for corresponding features in the video based on a semantic similarity of a given feature to others of the features. The CP module then generates a joint semantic vector for each of the features based at least on the semantic similarity of the given feature to one or more of the corresponding features and a spatiotemporal distance of the given feature to the one or more of the corresponding features. The artificial neural network is able to identify motion in the video using the joint semantic vectors generated for the features extracted from the video.
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公开(公告)号：US11062460B2
公开(公告)日：2021-07-13
申请号：US16274481
申请日：2019-02-13
Applicant: Adobe Inc.
Inventor： Xingyu Liu , Hailin Jin , Joonyoung Lee
Abstract: Technology is disclosed herein for learning motion in video. In an implementation, an artificial neural network extracts features from a video. A correspondence proposal (CP) module performs, for at least some of the features, a search for corresponding features in the video based on a semantic similarity of a given feature to others of the features. The CP module then generates a joint semantic vector for each of the features based at least on the semantic similarity of the given feature to one or more of the corresponding features and a spatiotemporal distance of the given feature to the one or more of the corresponding features. The artificial neural network is able to identify motion in the video using the joint semantic vectors generated for the features extracted from the video.
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公开(公告)号：US11055828B2
公开(公告)日：2021-07-06
申请号：US16407915
申请日：2019-05-09
Applicant: Adobe Inc.
Inventor： Mai Long , Zhaowen Wang , Ning Xu , John Philip Collomosse , Haotian Zhang , Hailin Jin
Abstract: Techniques of inpainting video content include training a neural network to perform an inpainting operation on a video using only content from that video. For example, upon receiving video content including a sequence of initial frames, a computer generates a sequence of inputs corresponding to at least some of the sequence of initial frames and each input including, for example, a uniform noise map. The computer then generates a convolutional neural network (CNN) using the sequence of input as the initial layer. The parameters of the CNN are adjusted according to a cost function, which has components including a flow generation loss component and a consistency loss component. The CNN then outputs, on a final layer, estimated image values in a sequence of final frames.
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公开(公告)号：US10755139B2
公开(公告)日：2020-08-25
申请号：US15494106
申请日：2017-04-21
Applicant: Adobe Inc.
Inventor： Hailin Jin , Kai Ni
Abstract: In one embodiment, a computer accessible storage medium stores a plurality of instructions which, when executed: group a set of reconstructed three dimensional (3D) points derived from image data into a plurality of groups based on one or more attributes of the 3D points; select one or more groups from the plurality of groups; and sample data from the selected groups, wherein the sampled data is input to a consensus estimator to generate a model that describes a 3D model of a scene captured by the image data. Other embodiments may bias sampling into a consensus estimator for any data set, based on relative quality of the data set.
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公开(公告)号：US20190325275A1
公开(公告)日：2019-10-24
申请号：US15957419
申请日：2018-04-19
Applicant: Adobe Inc.
Inventor： Joon-Young Lee , Hailin Jin , Fabian David Caba Heilbron
Abstract: Various embodiments describe active learning methods for training temporal action localization models used to localize actions in untrimmed videos. A trainable active learning selection function is used to select unlabeled samples that can improve the temporal action localization model the most. The select unlabeled samples are then annotated and used to retrain the temporal action localization model. In some embodiment, the trainable active learning selection function includes a trainable performance prediction model that maps a video sample and a temporal action localization model to a predicted performance improvement for the temporal action localization model.
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Patent Agency Ranking
公开(公告)号：US10453204B2
公开(公告)日：2019-10-22
申请号：US15676903
申请日：2017-08-14
Applicant: Adobe Inc.
Inventor： Zhaowen Wang , Hailin Jin
Abstract: The present disclosure is directed towards systems and methods for generating a new aligned image from a plurality of burst image. The systems and methods subdivide a reference image into a plurality of local regions and a subsequent image into a plurality of corresponding local regions. Additionally, the systems and methods detect a plurality of feature points in each of the reference image and the subsequent image and determine matching feature point pairs between the reference image and the subsequent image. Based on the matching feature point pairs, the systems and methods determine at least one homography of the reference image to the subsequent image. Based on the homography, the systems and methods generate a new aligned image that is that is pixel-wise aligned to the reference image. Furthermore, the systems and methods refines boundaries between local regions of the new aligned image.
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公开(公告)号：US10430455B2
公开(公告)日：2019-10-01
申请号：US15619205
申请日：2017-06-09
Applicant: Adobe Inc.
Inventor： Hailin Jin , John Philip Collomosse
IPC: G06K9/00 , G06F16/532 , G06K9/46
Abstract: Sketch and style based image retrieval in a digital medium environment is described. Initially, a user sketches an object (e.g., with a stylus) to be searched in connection with an image search. Styled images are selected to indicate a desired style of images to be returned by the search. A search request is generated based on the sketch and selected images. Responsive to the request, an image repository is searched to identify images having the desired object and styling. To search the image repository, a neural network is utilized that is capable of recognizing the desired object in images based on visual characteristics of the sketch and independently recognizing the desired styling in images based on visual characteristics of the selected images. This independent recognition allows desired styling to be specified by selecting images having the style but not the desired object. Images having the desired object and styling are returned.
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公开(公告)号：US20190286647A1
公开(公告)日：2019-09-19
申请号：US16432834
申请日：2019-06-05
Applicant: Adobe Inc.
Inventor： Hailin Jin , John Philip Collomosse
IPC: G06F16/532 , G06K9/62 , G06K9/46 , G06K9/00
Abstract: Sketch and style based image retrieval in a digital medium environment is described. Initially, a user sketches an object to be searched in connection with an image search. Styled images are selected to indicate a desired style of images to be returned by the search. A search request is generated based on the sketch and selected images. Responsive to the request, an image repository is searched to identify images having the desired object and styling. To search the image repository, a neural network is utilized that is capable of recognizing the desired object in images based on visual characteristics of the sketch and independently recognizing the desired styling in images based on visual characteristics of the selected images. This independent recognition allows desired styling to be specified by selecting images having the style but not the desired object. Images having the desired object and styling are returned.
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公开(公告)号：US10380462B2
公开(公告)日：2019-08-13
申请号：US16013791
申请日：2018-06-20
Applicant: Adobe Inc.
Inventor： Hailin Jin , Zhaowen Wang , Gavin Stuart Peter Miller
Abstract: Font replacement based on visual similarity is described. In one or more embodiments, a font descriptor includes multiple font features derived from a visual appearance of a font by a font visual similarity model. The font visual similarity model can be trained using a machine learning system that recognizes similarity between visual appearances of two different fonts. A source computing device embeds a font descriptor in a document, which is transmitted to a destination computing device. The destination compares the embedded font descriptor to font descriptors corresponding to local fonts. Based on distances between the embedded and the local font descriptors, at least one matching font descriptor is determined. The local font corresponding to the matching font descriptor is deemed similar to the original font. The destination computing device controls presentations of the document using the similar local font. Computation of font descriptors can be outsourced to a remote location.
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50.发明授权公开(公告)号：US10346727B2
公开(公告)日：2019-07-09
申请号：US15429769
申请日：2017-02-10
Applicant: Adobe Inc.
Inventor： Zhe Lin , Mai Long , Jonathan Brandt , Hailin Jin , Chen Fang
Abstract: The present disclosure includes methods and systems for searching for digital visual media based on semantic and spatial information. In particular, one or more embodiments of the disclosed systems and methods identify digital visual media displaying targeted visual content in a targeted region based on a query term and a query area provide via a digital canvas. Specifically, the disclosed systems and methods can receive user input of a query term and a query area and provide the query term and query area to a query neural network to generate a query feature set. Moreover, the disclosed systems and methods can compare the query feature set to digital visual media feature sets. Further, based on the comparison, the disclosed systems and methods can identify digital visual media portraying targeted visual content corresponding to the query term within a targeted region corresponding to the query area.
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