Adobe, Inc.

Ajay Jain

Sachin Soni

Sanjeev Tagra

Ryan Rozich

Jonathan Roeder

Prasenjit Mondal

Anurag Aggarwal

Ajay Bedi

Abhishek Jain

Eric Andrew Kienle

Ram Bhushan Agrawal

Nitish Maurya

Arshiya Aggarwal

Jingwan Lu

Nikaash Puri

Amit Srivastava

Mili Sharma

Siddharth Kumar

Sumeet Gupta

Carl Dockhorn

Niranjan Shivanand Kumbi

Mansukh Patidar

Ajay Awatramani

Shagun Kush

Anup Kumar

Nikita Kapoor

Guillaume Le Bleis

Mukul

Ashish Rawat

Anugrah Prakash

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

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

Techniques and systems are described that support collection of electronic signatures within an online conference. At least one user input is received as part of the online conference. The user input specifies an attendee of the online conference that is provide an electronic signature. A signature module is communicated within the online conference. The signature module is configured to receive the electronic signature from the attendee. The electronic signature received via the signature module from the attendee within the online conference is applied to the digital content.

Electronic signature collection within an online conference

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

A method of generating accessible content is described. Embodiments of the method identifies a plurality of channels for a multimedia communication session, generate a master timeline for the communication session, wherein the master timeline comprises a chronological ordering of events from each of the channels, and wherein each of the events is associated with event-specific audio data, and present the multimedia communication session to a user to enable the user to transition among the channels based on the master timeline.

Accessible multimedia content

Systems and methods for removing objects from images are disclosed. An image processing application identifies a boundary of each object of a set of objects in an image. The image processing application identifies a completed boundary for each object of the set of objects by providing the object to a trained model. The image processing application determines a set of masks. Each mask corresponds to an object of the set of objects and represents a region of the image defined by an intersection of the boundary of the object and the boundary of a target object to be removed from the image. The image processing application updates each mask by separately performing content filling on the corresponding region. The image processing application creates an output image by merging each of the updated masks with portions of the image.

Boundary-aware object removal and content fill

Methods, apparatus, and systems are provided for obfuscating facial identity in images by synthesizing a new facial image for an input image. A base face is detected from or selected for an input image. Facial images that are similar to the base face are selected and combined to create a new facial image. The new facial image is added to the input image such that the input image includes a combination of the base face and the new facial image. Where no base face is detected in the input image, a base face is selected from reference facial images based at least on pose keypoints identified in the input image. After a new facial image is generated based on the selected base face, a combination of the new facial image and the base facial image are added to the input image by aligning one or more pose keypoints.

Identity obfuscation in images utilizing synthesized faces

The present disclosure relates to a cloud-based system including a server for generating and nurturing leads using within-a document lead nurturing. The server enables a marketer to generate initial content and contextual content, specify a consumption condition with respect to the initial content, and generate an electronic file including the initial content and executable code configured to monitor consumption of the initial content. A user (e.g., possible lead) uses a reader program of their device to open the electronic file, which presents the initial content and triggers execution of an instruction of the executable code to monitor interactions of the user with the initial content to determine whether the consumption condition has been satisfied. The reader program executes another instruction of the executable code to present the contextual content and notify the server that the user is a possible lead, when the consumption condition is satisfied.

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Ghaziabad, India

Ajay Jain