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公开(公告)号：US20200380027A1
公开(公告)日：2020-12-03
申请号：US16426369
申请日：2019-05-30
Applicant: Adobe Inc.
Inventor： Pranav Vineet Aggarwal , Zhe Lin , Baldo Antonio Faieta , Saeid Motiian
IPC: G06F16/583 , G06N3/08 , G06N20/00 , G06F16/33 , G06F16/538 , G06F16/532
Abstract: Multi-modal differential search with real-time focus adaptation techniques are described that overcome the challenges of conventional techniques in a variety of ways. In one example, a model is trained to support a visually guided machine-learning embedding space that supports visual intuition as to “what” is represented by text. The visually guided language embedding space supported by the model, once trained, may then be used to support visual intuition as part of a variety of functionality. In one such example, the visually guided language embedding space as implemented by the model may be leveraged as part of a multi-modal differential search to support search of digital images and other digital content with real-time focus adaptation which overcomes the challenges of conventional techniques.
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公开(公告)号：US10755099B2
公开(公告)日：2020-08-25
申请号：US16189805
申请日：2018-11-13
Applicant: Adobe Inc.
Inventor： Zhe Lin , Xiaohui Shen , Mingyang Ling , Jianming Zhang , Jason Wen Yong Kuen
Abstract: In implementations of object detection in images, object detectors are trained using heterogeneous training datasets. A first training dataset is used to train an image tagging network to determine an attention map of an input image for a target concept. A second training dataset is used to train a conditional detection network that accepts as conditional inputs the attention map and a word embedding of the target concept. Despite the conditional detection network being trained with a training dataset having a small number of seen classes (e.g., classes in a training dataset), it generalizes to novel, unseen classes by concept conditioning, since the target concept propagates through the conditional detection network via the conditional inputs, thus influencing classification and region proposal. Hence, classes of objects that can be detected are expanded, without the need to scale training databases to include additional classes.
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公开(公告)号：US10747811B2
公开(公告)日：2020-08-18
申请号：US15986401
申请日：2018-05-22
Applicant: Adobe Inc.
Inventor： Xiaohui Shen , Zhe Lin , Kalyan Krishna Sunkavalli , Hengshuang Zhao , Brian Lynn Price
Abstract: Compositing aware digital image search techniques and systems are described that leverage machine learning. In one example, a compositing aware image search system employs a two-stream convolutional neural network (CNN) to jointly learn feature embeddings from foreground digital images that capture a foreground object and background digital images that capture a background scene. In order to train models of the convolutional neural networks, triplets of training digital images are used. Each triplet may include a positive foreground digital image and a positive background digital image taken from the same digital image. The triplet also contains a negative foreground or background digital image that is dissimilar to the positive foreground or background digital image that is also included as part of the triplet.
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公开(公告)号：US10740647B2
公开(公告)日：2020-08-11
申请号：US15921492
申请日：2018-03-14
Applicant: Adobe Inc.
Inventor： Delun Du , Zhe Lin , Baldo Faieta
Abstract: The present disclosure is directed toward systems and methods for detecting an object in an input image based on a target object keyword. For example, one or more embodiments described herein generate a heat map of the input image based on the target object keyword and generate various bounding boxes based on a pixel analysis of the heat map. One or more embodiments described herein then utilize the various bounding boxes to determine scores for generated object location proposals in order to provide a highest scoring object location proposal overlaid on the input image.
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公开(公告)号：US10733480B2
公开(公告)日：2020-08-04
申请号：US16039311
申请日：2018-07-18
Applicant: Adobe Inc.
Inventor： Jayant Kumar , Zhe Lin , Vipulkumar C. Dalal
IPC: G06K9/62
Abstract: There is described a computing device and method in a digital medium environment for custom auto tagging of multiple objects. The computing device includes an object detection network and multiple image classification networks. An image is received at the object detection network and includes multiple visual objects. First feature maps are applied to the image at the object detection network and generate object regions associated with the visual objects. The object regions are assigned to the multiple image classification networks, and each image classification network is assigned to a particular object region. The second feature maps are applied to each object region at each image classification network, and each image classification network outputs one or more classes associated with a visual object corresponding to each object region.
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Patent Agency Ranking
公开(公告)号：US20200226724A1
公开(公告)日：2020-07-16
申请号：US16246051
申请日：2019-01-11
Applicant: Adobe Inc.
Inventor： Chen Fang , Zhe Lin , Zhaowen Wang , Yulun Zhang , Yilin Wang , Jimei Yang
Abstract: In implementations of transferring image style to content of a digital image, an image editing system includes an encoder that extracts features from a content image and features from a style image. A whitening and color transform generates coarse features from the content and style features extracted by the encoder for one pass of encoding and decoding. Hence, the processing delay and memory requirements are low. A feature transfer module iteratively transfers style features to the coarse feature map and generates a fine feature map. The image editing system fuses the fine features with the coarse features, and a decoder generates an output image with content of the content image in a style of the style image from the fused features. Accordingly, the image editing system efficiently transfers an image style to image content in real-time, without undesirable artifacts in the output image.
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公开(公告)号：US10565518B2
公开(公告)日：2020-02-18
申请号：US14748059
申请日：2015-06-23
Applicant: Adobe Inc.
Inventor： Hailin Jin , Chen Fang , Jianchao Yang , Zhe Lin
Abstract: The present disclosure is directed to collaborative feature learning using social media data. For example, a machine learning system may identify social media data that includes user behavioral data, which indicates user interactions with content item. Using the identified social user behavioral data, the machine learning system may determine latent representations from the content items. In some embodiments, the machine learning system may train a machine-learning model based on the latent representations. Further, the machine learning system may extract features of the content item from the trained machine-learning model.
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公开(公告)号：US20190333198A1
公开(公告)日：2019-10-31
申请号：US15962735
申请日：2018-04-25
Applicant: Adobe Inc.
Inventor： Yilin Wang , Zhe Lin , Zhaowen Wang , Xin Lu , Xiaohui Shen , Chih-Yao Hsieh
IPC: G06T5/50
Abstract: The present disclosure relates to training and utilizing an image exposure transformation network to generate a long-exposure image from a single short-exposure image (e.g., still image). In various embodiments, the image exposure transformation network is trained using adversarial learning, long-exposure ground truth images, and a multi-term loss function. In some embodiments, the image exposure transformation network includes an optical flow prediction network and/or an appearance guided attention network. Trained embodiments of the image exposure transformation network generate realistic long-exposure images from single short-exposure images without additional information.
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公开(公告)号：US20190332937A1
公开(公告)日：2019-10-31
申请号：US16507960
申请日：2019-07-10
Applicant: Adobe Inc.
Inventor： Zhe Lin , Yufei Wang , Scott Cohen , Xiaohui Shen
Abstract: Provided are systems and techniques that provide an output phrase describing an image. An example method includes creating, with a convolutional neural network, feature maps describing image features in locations in the image. The method also includes providing a skeletal phrase for the image by processing the feature maps with a first long short-term memory (LSTM) neural network trained based on a first set of ground truth phrases which exclude attribute words. Then, attribute words are provided by processing the skeletal phrase and the feature maps with a second LSTM neural network trained based on a second set of ground truth phrases including words for attributes. Then, the method combines the skeletal phrase and the attribute words to form the output phrase.
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公开(公告)号：US10460214B2
公开(公告)日：2019-10-29
申请号：US15799395
申请日：2017-10-31
Applicant: Adobe Inc.
Inventor： Xin Lu , Zhe Lin , Xiaohui Shen , Jimei Yang , Jianming Zhang , Jen-Chan Jeff Chien , Chenxi Liu
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for segmenting objects in digital visual media utilizing one or more salient content neural networks. In particular, in one or more embodiments, the disclosed systems and methods train one or more salient content neural networks to efficiently identify foreground pixels in digital visual media. Moreover, in one or more embodiments, the disclosed systems and methods provide a trained salient content neural network to a mobile device, allowing the mobile device to directly select salient objects in digital visual media utilizing a trained neural network. Furthermore, in one or more embodiments, the disclosed systems and methods train and provide multiple salient content neural networks, such that mobile devices can identify objects in real-time digital visual media feeds (utilizing a first salient content neural network) and identify objects in static digital images (utilizing a second salient content neural network).
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