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1. 发明申请

US20220415084A1 FINDING SIMILAR PERSONS IN IMAGES 有权
公开(公告)号：US20220415084A1
公开(公告)日：2022-12-29
申请号：US17902349
申请日：2022-09-02
申请人： Adobe Inc.
发明人： Saeid MOTIIAN , Zhe LIN , Shabnam GHADAR , Baldo FAIETA
IPC分类号： G06V40/16 , G06K9/62 , G06V30/194 , G06V40/10 , G06K9/00
摘要： 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.

2. 发明申请

US20220237830A1 AUTOMATIC OBJECT RE-COLORIZATION 有权
公开(公告)号：US20220237830A1
公开(公告)日：2022-07-28
申请号：US17155570
申请日：2021-01-22
申请人： Adobe Inc.
发明人： Siavash KHODADADEH , Zhe LIN , Shabnam GHADAR , Saeid MOTIIAN , Richard ZHANG , Ratheesh KALAROT , Baldo FAIETA
IPC分类号： G06T11/00 , G06T7/90 , G06N3/04 , G06N3/08
摘要： Embodiments are disclosed for automatic object re-colorization in images. In some embodiments, a method of automatic object re-colorization includes receiving a request to recolor an object in an image, the request including an object identifier and a color identifier, identifying an object in the image associated with the object identifier, generating a mask corresponding to the object in the image, providing the image, the mask, and the color identifier to a color transformer network, the color transformer network trained to recolor objects in input images, and generating, by the color transformer network, a recolored image, wherein the object in the recolored image has been recolored to a color corresponding to the color identifier

3. 发明申请

US20230079886A1 LABELING TECHNIQUES FOR A MODIFIED PANOPTIC LABELING NEURAL NETWORK 有权
公开(公告)号：US20230079886A1
公开(公告)日：2023-03-16
申请号：US18048311
申请日：2022-10-20
申请人： Adobe Inc.
发明人： Sohrab AMIRGHODSI , Zhe LIN , Yilin WANG , Tianshu YU , Connelly BARNES , Elya SHECHTMAN
IPC分类号： G06K9/62 , G06F17/18 , G06N3/08 , G06N20/00 , G06V10/75 , G06V10/82
摘要： 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.

4. 发明申请

US20220300729A1 FINDING SIMILAR PERSONS IN IMAGES 有权
公开(公告)号：US20220300729A1
公开(公告)日：2022-09-22
申请号：US17207178
申请日：2021-03-19
申请人： Adobe Inc.
发明人： Saeid MOTIIAN , Zhe LIN , Shabnam GHADAR , Baldo FAIETA
IPC分类号： G06K9/00 , G06K9/66 , G06K9/62
摘要： 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.

5. 发明申请

US20220101531A1 ENHANCED SEMANTIC SEGMENTATION OF IMAGES 有权
公开(公告)号：US20220101531A1
公开(公告)日：2022-03-31
申请号：US17479646
申请日：2021-09-20
申请人： ADOBE INC.
发明人： Jianming ZHANG , Zhe LIN
IPC分类号： G06T7/11 , G06N3/04
摘要： 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.

6. 发明申请

US20210342983A1 ITERATIVE IMAGE INPAINTING WITH CONFIDENCE FEEDBACK 有权
公开(公告)号：US20210342983A1
公开(公告)日：2021-11-04
申请号：US16861548
申请日：2020-04-29
申请人： ADOBE INC.
发明人： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
IPC分类号： G06T5/00 , G06N3/04 , G06N3/08
摘要： 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.

7. 发明申请

US20220366546A1 ITERATIVE IMAGE INPAINTING WITH CONFIDENCE FEEDBACK 有权
公开(公告)号：US20220366546A1
公开(公告)日：2022-11-17
申请号：US17812639
申请日：2022-07-14
申请人： ADOBE INC.
发明人： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
IPC分类号： G06T5/00 , G06N3/04 , G06N3/08
摘要： 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.

8. 发明申请

US20210342984A1 GUIDED UP-SAMPLING FOR IMAGE INPAINTING 有权
公开(公告)号：US20210342984A1
公开(公告)日：2021-11-04
申请号：US16864388
申请日：2020-05-01
申请人： ADOBE INC.
发明人： Zhe LIN , Yu ZENG , Jimei YANG , Jianming ZHANG , Elya SHECHTMAN
IPC分类号： G06T5/00 , G06T3/40
摘要： 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.

9. 发明申请

US20210082118A1 ENHANCED SEMANTIC SEGMENTATION OF IMAGES 有权
公开(公告)号：US20210082118A1
公开(公告)日：2021-03-18
申请号：US16574513
申请日：2019-09-18
申请人： ADOBE INC.
发明人： Jianming ZHANG , Zhe LIN
IPC分类号： G06T7/11 , G06N3/04
摘要： 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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