Adobe, Inc.

Adobe Systems Inc.

Adobeinc.

Neolane Sas

Sachin Soni

Anmol Dhawan

Ashish Duggal

Ajay Jain

Sanjeev Tagra

Ryan Rozich

Prasenjit Mondal

Jonathan Roeder

Ganesh Sahai

Sachin Gaur

Stephane Moreau

Ajay Bedi

Abhishek Jain

Stéphane Moreau

Kevin Gary Smith

Russell Robert Stringham

Walter Chang

Walter Wei-Tuh Chang

Vineet Sharma

Eric Andrew Kienle

Nikaash Puri

Carl Dockhorn

Vikas Yadav

Amit Srivastava

Shagun Kush

Arshiya Aggarwal

Nitish Maurya

Jingwan Lu

Franck Dernoncourt

Ajinkya Gorakhnath Kale

Trung Huu Bui

Shawn Alan Gaither

Ram Bhushan Agrawal

Nikolaos Barmpalios

Olivier Sirven

Anuj Jain

Sasha Spala

Nicholas Miller

Yu Gong

Siddharth Kumar

Suryadeep Kumar Agrawal

Samarth Gupta

Naqushab Neyazee

Tushar Dublish

Joon Kim

Viswanathan Swaminathan

Abhishek Kumar

Naveen Prakash Goel

Niranjan Shivanand Kumbi

Cedric Huesler

Anurag Aggarwal

Ajay Awatramani

John Thomas Kucera

Himanshu Mittal

Martin Buergi

Tobias Martin Bocanegra Alvarez

Nikita Kapoor

Guillaume Le Bleis

Sankalp Raghav

Tanvi Rastogi

Ashish Rawat

Anugrah Prakash

Anshul Malik

Embodiments are disclosed for providing customizable, visually aesthetic color diverse template recommendations derived from a source image. A method may include receiving a source image and determining a source image background by separating a foreground of the source image from a background of the source image. The method separates a foreground from the background by identifying portions of the image that belong to the background and stripping out the rest of the image. The method includes identifying a text region of the source image using a machine learning model and identifying font type using the identified text region. The method includes generating an editable template image using the source image background, the text region, and the font type.

Automatic template recommendation

An image search system uses a multi-modal model to determine relevance of images to a spoken query. The multi-modal model includes a spoken language model that extracts features from spoken query and a language processing model that extract features from an image. The multi-model model determines a relevance score for the image and the spoken query based on the extracted features. The multi-modal model is trained using a curriculum approach that includes training the spoken language model using audio data. Subsequently, a training dataset comprising a plurality of spoken queries and one or more images associated with each spoken query is used to jointly train the spoken language model and an image processing model to provide a trained multi-modal model.

Spoken query processing for image search

Techniques are disclosed for generating image recommendations to facilitate the sale of a product. An example methodology includes identifying a product category associated with an image of the product provided by the seller, and a product sub-category associated with the product image. The method further includes retrieving one or more images of for-sale items. The retrieval is based on a search of for-sale listings using the identified product category and the identified product sub-category. The method further includes clustering the retrieved images of for-sale items into groups, each group associated with a perspective viewpoint of the for-sale item. The method further includes providing a selected image from each group as an image recommendation. The selection is based on a value score associated with each of the images of the for-sale items. A graphical status indicating completeness of the seller's image set is updated in response to recommended images being adopted.

Generation of recommendations for visual product details

The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement an image filter for enhancing light text and removing document shadows. In particular embodiments, the disclosed systems use a modified adaptive thresholding approach the relies on image gradients to efficiently guide the thresholding process. In addition, the disclosed systems use a machine-learning model to generate a document shadow map. The document shadow map can include text reflections. Accordingly, the disclosed systems remove text reflections from the document shadow map (e.g., by using an interpolated shadow intensity value of neighboring shadow map pixels). In turn, the disclosed systems use the document text mask and the document shadow map cleaned of text reflections to remove shadows from the digital image. Further, the disclosed systems enhance text in the shadow-removed digital image based on contrast stretching.

Enhancing light text in scanned documents while preserving document fidelity

Techniques are disclosed for identifying asides within a document, and detecting a display order of contents based of the identified asides. In a document, an 'aside' represents a content region of the document that is distinct from the main content regions, and may be visually distinguishable from the main content region. In an example, a document is received, where the document lacks identification of asides. The document is analyzed to identify asides within the document. A display order of contents within the document is then determined, based on the identified asides. For example, in the display order, the asides are ordered between two segments of the main content and/or at a beginning or an end of the main content, but may not be ordered to be embedded in between a segment of the main content. The document is displayed in accordance with the display order.

Asides detection in documents

Techniques and systems are described for performing semantic text searches. A semantic text-searching solution uses a machine learning system (such as a deep learning system) to determine associations between the semantic meanings of words. These associations are not limited by the spelling, syntax, grammar, or even definition of words. Instead, the associations can be based on the context in which characters, words, and/or phrases are used in relation to one another. In response to detecting a request to locate text within an electronic document associated with a keyword, the semantic text-searching solution can return strings within the document that have matching and/or related semantic meanings or contexts, in addition to exact matches (e.g., string matches) within the document. The semantic text-searching solution can then output an indication of the matching strings.

Model-based semantic text searching

An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.

Machine learning techniques for differentiability scoring of digital images

Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.

Machine learning for digital image selection across object variations

Methods, systems, and non-transitory computer readable media are disclosed for generating a composite image comprising objects in positions from two or more different digital images. In one or more embodiments, the disclosed system receives a sequence of images and identifies objects within the sequence of images. In one example, the disclosed system determines a target position for a first object based on detecting user selection of the first object in the target position from a first image. The disclosed system can generate a fixed object image comprising the first object in the target position. The disclosed system can generate preview images comprising the fixed object image with the second object sequencing through a plurality of positions as seen in the sequence of images. Based on a second user selection of a desired preview image, the disclosed system can generate the composite image.

Generating composite images with objects from different times

Certain embodiments involve visually augmenting images of three-dimensional containers with virtual elements that fill one or more empty regions of the three-dimensional containers. For instance, a computing system receives a first image that depicts a storage container and identify sub-containers within the storage container. The computing system selects, from a virtual object library, a plurality of virtual objects that are semantically related to the sub-container. The computing system determines an arrangement of the virtual objects within the sub-container based on semantics associated with the sub-container and the plurality of virtual objects. The computing system generates a second image that depicts the arrangement of the plurality of virtual objects within the storage container and sub-containers. The computing system generates, for display, the second image depicting the storage container and the arrangement of the virtual objects.

Visually augmenting images of three-dimensional containers with virtual elements

A framework generates a thumbnail to represent a video on a webpage based on a prominent individual appearing in both the video and content of the webpage. Content of a webpage on which a video is to be posted is analyzed to identify individuals represented in the webpage content. Frames of the video are also analyzed to identify individuals in the video. A first individual that appears in both the webpage content and the video is selected based on a score for the first individual determined based on the webpage content. Subsequent to selecting the first individual, frames of the video that include the first individual are analyzed to select a first frame of the video that includes the first individual. A thumbnail to represent the video on the webpage is generated from the first frame, and the thumbnail is provided for presentation on the webpage to represent the video.

Customized selection of video thumbnails to present on social media webpages

In implementations of systems for role classification, a computing device implements a role system to receive data describing a corpus of text that is associated with a user ID. Feature values of features are generated by a first machine learning model by processing the corpus of text, the features representing questions with respect to the corpus of text and the feature values representing answers to the questions included in the corpus of text. A classification of a role is generated by a second machine learning model by processing the feature values, the classification of the role indicating a relationship of the user ID with respect to a product or service. The role system outputs an indication of the classification of the role for display in a user interface of a display device.

Systems for role classification

Methods and systems are provided for providing directional assistance to guide a user to position a camera for centering a person's face within the camera's field of view. A neural network system is trained to determine the position of the user's face relative to the center of the field of view as captured by an input image. The neural network system is trained using training input images that are generated by cropping different regions of initial training images. Each initial image is used to create a plurality of different training input images, and directional assistance labels used to train the network may be assigned to each training input image based on how the image is cropped. Once trained, the neural network system determines a position of the user's face, and automatically provides a non-visual prompt indicating how to center the face within the field of view.

Directional assistance for centering a face in a camera field of view

Techniques are provided herein for generating improved document summaries that consider the amount of time that has passed since the user last accessed the document. The length of time that has passed since the user has accessed each previous portion of the document is used as a variable to determine how much the summary should focus on each of the previously read sections of the document. When a document is accessed by a user, a relevance score is assigned to content from previously accessed sections of that document, where the relevance score is weighted based on how long ago each of the sections was accessed by the user. Once the various content items of previous sections have been provided relevance scores, selected sentences with the highest relevance scores are fed to a deep learning sequence-to-sequence model is used to build the document summary.

Generating time-based recaps of documents using a deep learning sequence to sequence model

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New Delhi, India

Sachin Soni