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公开(公告)号：US20230079886A1
公开(公告)日：2023-03-16
申请号：US18048311
申请日：2022-10-20
Applicant: Adobe Inc.
Inventor： Sohrab AMIRGHODSI , Zhe LIN , Yilin WANG , Tianshu YU , Connelly BARNES , Elya SHECHTMAN
Abstract: A panoptic labeling system includes a modified panoptic labeling neural network (“modified PLNN”) that is trained to generate labels for pixels in an input image. The panoptic labeling system generates modified training images by combining training images with mask instances from annotated images. The modified PLNN determines a set of labels representing categories of objects depicted in the modified training images. The modified PLNN also determines a subset of the labels representing categories of objects depicted in the input image. For each mask pixel in a modified training image, the modified PLNN calculates a probability indicating whether the mask pixel has the same label as an object pixel. The modified PLNN generates a mask label for each mask pixel, based on the probability. The panoptic labeling system provides the mask label to, for example, a digital graphics editing system that uses the labels to complete an infill operation.
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公开(公告)号：US20220300729A1
公开(公告)日：2022-09-22
申请号：US17207178
申请日：2021-03-19
Applicant: Adobe Inc.
Inventor： Saeid MOTIIAN , Zhe LIN , Shabnam GHADAR , Baldo FAIETA
Abstract: Embodiments are disclosed for finding similar persons in images. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an image query, the image query including an input image that includes a representation of a person, generating a first cropped image including a representation of the person's face and a second cropped image including a representation of the person's body, generating an image embedding for the input image by combining a face embedding corresponding to the first cropped image and a body embedding corresponding to the second cropped image, and querying an image repository in embedding space by comparing the image embedding to a plurality of image embeddings associated with a plurality of images in the image repository to obtain one or more images based on similarity to the input image in the embedding space.
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公开(公告)号：US20220101531A1
公开(公告)日：2022-03-31
申请号：US17479646
申请日：2021-09-20
Applicant: ADOBE INC.
Inventor： Jianming ZHANG , Zhe LIN
Abstract: Enhanced methods and systems for the semantic segmentation of images are described. A refined segmentation mask for a specified object visually depicted in a source image is generated based on a coarse and/or raw segmentation mask. The refined segmentation mask is generated via a refinement process applied to the coarse segmentation mask. The refinement process correct at least a portion of both type I and type II errors, as well as refine boundaries of the specified object, associated with the coarse segmentation mask. Thus, the refined segmentation mask provides a more accurate segmentation of the object than the coarse segmentation mask. A segmentation refinement model is employed to generate the refined segmentation mask based on the coarse segmentation mask. That is, the segmentation model is employed to refine the coarse segmentation mask to generate more accurate segmentations of the object. The refinement process is an iterative refinement process carried out via a trained neural network.
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公开(公告)号：US20210342983A1
公开(公告)日：2021-11-04
申请号：US16861548
申请日：2020-04-29
Applicant: ADOBE INC.
Inventor： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators). The image inpainting system can use such networks to predict an inpainting image result that fills the hole, region, and/or portion of the image using predicted pixels and generates a corresponding confidence map of the predicted pixels.
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公开(公告)号：US20250054115A1
公开(公告)日：2025-02-13
申请号：US18232212
申请日：2023-08-09
Applicant: Adobe Inc.
Inventor： Zhe LIN , Yuqian ZHOU , Sohrab AMIRGHODSI , Qing LIU , Elya SHECHTMAN , Connelly BARNES , Haitian ZHENG
Abstract: Various disclosed embodiments are directed to resizing, via down-sampling and up-sampling, a high-resolution input image in order to meet machine learning model low-resolution processing requirements, while also producing a high-resolution output image for image inpainting via a machine learning model. Some embodiments use a refinement model to refine the low-resolution inpainting result from the machine learning model such that there will be clear content with high resolution both inside and outside of the mask region in the output. Some embodiments employ new model architecture for the machine learning model that produces the inpainting result—an advanced Cascaded Modulated Generative Adversarial Network (CM-GAN) that includes Fast Fourier Convolution (FCC) layers at the skip connections between the encoder and decoder.
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Patent Agency Ranking
公开(公告)号：US20240371007A1
公开(公告)日：2024-11-07
申请号：US18770386
申请日：2024-07-11
Applicant: Adobe Inc.
Inventor： Zhe LIN , Simon Su Chen , Jason wen-young Kuen , Bo Sun
Abstract: Various disclosed embodiments are directed to refining or correcting individual semantic segmentation/instance segmentation masks that have already been produced by baseline models in order to generate a final coherent panoptic segmentation map. Specifically, a refinement model, such as an encoder-decoder-based neural network, generates or predicts various data objects, such as foreground masks, bounding box offset maps, center maps, center offset maps, and coordinate convolution. This, among other functionality described herein, improves the inaccuracies and computing resource consumption of existing technologies.
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公开(公告)号：US20240037939A1
公开(公告)日：2024-02-01
申请号：US18487183
申请日：2023-10-16
Applicant: ADOBE INC.
Inventor： Quan Hung TRAN , Long Thanh MAI , Zhe LIN , Zhuowan LI
IPC: G06V20/30 , G06F16/55 , G06F16/535 , G06F40/205 , G06V10/75 , G06F18/214 , G06V10/82
CPC classification number: G06V20/30 , G06F16/55 , G06F16/535 , G06F40/205 , G06V10/751 , G06F18/214 , G06V10/82
Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.
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8.发明公开公开(公告)号：US20230214600A1
公开(公告)日：2023-07-06
申请号：US18182068
申请日：2023-03-10
Applicant: Adobe Inc.
Inventor： Zhe LIN , Walter W. CHANG , Scott COHEN , Khoi Viet PHAM , Jonathan BRANDT , Franck DERNONCOURT
IPC: G06F40/30 , G06F16/532 , G06F16/55 , G06N5/02 , G06N5/04 , G06F40/205 , G06F40/295 , G06N20/00
CPC classification number: G06F40/30 , G06F16/532 , G06F16/55 , G06N5/02 , G06N5/04 , G06F40/205 , G06F40/295 , G06N20/00
Abstract: Embodiments of the present invention provide systems, methods, and non-transitory computer storage media for parsing a given input referring expression into a parse structure and generating a semantic computation graph to identify semantic relationships among and between objects. At a high level, when embodiments of the preset invention receive a referring expression, a parse tree is created and mapped into a hierarchical subject, predicate, object graph structure that labeled noun objects in the referring expression, the attributes of the labeled noun objects, and predicate relationships (e.g., verb actions or spatial propositions) between the labeled objects. Embodiments of the present invention then transform the subject, predicate, object graph structure into a semantic computation graph that may be recursively traversed and interpreted to determine how noun objects, their attributes and modifiers, and interrelationships are provided to downstream image editing, searching, or caption indexing tasks.
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公开(公告)号：US20220366546A1
公开(公告)日：2022-11-17
申请号：US17812639
申请日：2022-07-14
Applicant: ADOBE INC.
Inventor： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of images using iterative image inpainting. In particular, iterative inpainting utilize a confidence analysis of predicted pixels determined during the iterations of inpainting. For instance, a confidence analysis can provide information that can be used as feedback to progressively fill undefined pixels that comprise the holes, regions, and/or portions of an image where information for those respective pixels is not known. To allow for accurate image inpainting, one or more neural networks can be used. For instance, a coarse result neural network (e.g., a GAN comprised of a generator and a discriminator) and a fine result neural network (e.g., a GAN comprised of a generator and two discriminators). The image inpainting system can use such networks to predict an inpainting image result that fills the hole, region, and/or portion of the image using predicted pixels and generates a corresponding confidence map of the predicted pixels.
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公开(公告)号：US20210342984A1
公开(公告)日：2021-11-04
申请号：US16864388
申请日：2020-05-01
Applicant: ADOBE INC.
Inventor： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
Abstract: Methods and systems are provided for accurately filling holes, regions, and/or portions of high-resolution images using guided upsampling during image inpainting. For instance, an image inpainting system can apply guided upsampling to an inpainted image result to enable generation of a high-resolution inpainting result from a lower-resolution image that has undergone inpainting. To allow for guided upsampling during image inpainting, one or more neural networks can be used. For instance, a low-resolution result neural network (e.g., comprised of an encoder and a decoder) and a high-resolution input neural network (e.g., comprised of an encoder and a decoder). The image inpainting system can use such networks to generate a high-resolution inpainting image result that fills the hole, region, and/or portion of the image.
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