Adobe, Inc.

Balaji Krishnamurthy

Nikaash Puri

Mausoom Sarkar

Nupur Kumari

Piyush Gupta

Milan Aggarwal

Mayank Kumar Singh

Ayush Chopra

Akash Rupela

Abhishek Sinha

Mayur Hemani

Anubha Rastogi

Mihir Naware

Surgan Jandial

Yaman Kumar

Shagun Sodhani

Hyman Chung

Pankhri Singhai

Vikas Yadav

Kumar Mrityunjay Singh

Jonas Dahl

Rajiv Mangla

Kunal Kumar Jain

Gitesh Malik

Puneet Mangla

Sukriti Verma

Eshita Shah

Sundeep Parsa

Sumit Bhatia

Kumar Ayush

Arneh Jain

Hiresh Gupta

Matthew William Rozen

Simra Shahid

Jayakumar Subramanian

Somesh Singh

Pranjal Prasoon

William Brandon George

Amit Gupta

Deepak Pai

Pinkesh Badjatiya

Lauren Dest

Joshua Butikofer

Shankar Venkitachalam

Shripad Vilasrao Deshmukh

Brandon Pack

Vijay Srivastava

Rachit Bansal

Vinh Ngoc Khuc

Nimish Srivastav

Abhinav Java

Karanjeet Singh

Bishal Deb

Ankita Sarkar

Dhruv Khattar

Apoorva Gupta

Clinton Hansen Goudie-Nice

Patel Utkarsh Govind

Anirudh Singhal

Rishabh Jain

Jivat Neet Kaur

Tushar Singla

Shirsh Bansal

Sean Steimer

Florin Silviu Iordache

Horea Bacila

Umang Moorarka

Siddarth R

Kartikay Garg

Suman Basetty

James Patrick O'Reilly

Sandipan Karmakar

Abhinav Chauhan

Mihai Corlan

Ganesh Karbhari Palwe

Alexander Klimetschek

Timothy Chia-chi Liu

Pratiksha Agarwal

Meet Patel

Georgios Theocharous

Harpreet Singh

Nitin Goel

Gajinder Singh

Tanay Anand

Praveen Kumar Goyal

Atul Kumar Shrivastava

Nikitha Srikanth

S Sejal Naidu

Sharat Chandra Racha

Manoj Awasthi

Sunandini Basu

Kapil Malik

Arun Kumar

Parth Patel

Abhijeet Kumar

Siddhartha Gairola

Sriyash Poddar

Aahitagni Mukherjee

Dheeraj Bansal

Content relevance based table query answering is described. In one or more examples, a query and a table are received. The table includes a plurality of cells. A plurality of scores for calculated that correspond to the plurality of cells based on the query. One or more machine-learning models are then leveraged to generate a search result from the query, table, and scores, which is presented in a user interface for display.

Content relevance based table query answering

Systems and methods for data augmentation are provided. One aspect of the systems and methods include receiving an image that is misclassified by a classification network; computing an augmentation image based on the image using an augmentation network; and generating an augmented image by combining the image and the augmentation image, wherein the augmented image is correctly classified by the classification network.

Systems and methods for data augmentation

In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.

Digital content analysis

This disclosure describes one or more implementations of systems, non-transitory computer-readable media, and methods that utilize a local implicit image function neural network to perform image segmentation with a continuous class label probability distribution. For example, the disclosed systems utilize a local-implicit-image-function (LIIF) network to learn a mapping from an image to its semantic label space. In some instances, the disclosed systems utilize an image encoder to generate an image vector representation from an image. Subsequently, in one or more implementations, the disclosed systems utilize the image vector representation with a LIIF network decoder that generates a continuous probability distribution in a label space for the image to create a semantic segmentation mask for the image. Moreover, in some embodiments, the disclosed systems utilize the LIIF-based segmentation network to generate segmentation masks at different resolutions without changes in an input resolution of the segmentation network.

Utilizing implicit neural representations to parse visual components of subjects depicted within visual content

Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.

Determining user affinities for content generation applications

A corrective noise system receives an electronic version of a fillable form generated by a segmentation network and receives a correction to a segmentation error in the electronic version of the fillable form. The corrective noise system is trained to generate noise that represents the correction and superimpose the noise on the fillable form. The corrective noise system is further trained to identify regions in a corpus of forms that are semantically similar to a region that was subject to the correction. The generated noise is propagated to the semantically similar regions in the corpus of forms and the noisy corpus of forms is provided as input to the segmentation network. The noise causes the segmentation network to accurately identify fillable regions in the corpus of forms and output a segmented version of the corpus of forms having improved fidelity without retraining or otherwise modifying the segmentation network.

Personalized form error correction propagation

Various embodiments classify one or more portions of an image based on deriving an 'intrinsic' modality. Such intrinsic modality acts as a substitute to a “text” modality in a multi-modal network. A text modality in image processing is typically a natural language text that describes one or more portions of an image. However, explicit natural language text may not be available across one or more domains for training a multi-modal network. Accordingly, various embodiments described herein generate an intrinsic modality, which is also a description of one or more portions of an image, except that such description is not an explicit natural language description, but rather a machine learning model representation. Some embodiments additionally leverage a visual modality obtained from a vision-only model or branch, which may learn domain characteristics that are not present in the multi-modal network. Some embodiments additionally fuse or integrate the intrinsic modality with the visual modality for better generalization.

Using intrinsic multimodal features of image for domain generalized

Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.

Generating commonsense context for text using knowledge graphs

Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.

System and methods for neural topic modeling using topic attention networks

Systems, methods, and computer storage media are disclosed for predicting visual compatibility between a bundle of catalog items (e.g., a partial outfit) and a candidate catalog item to add to the bundle. Visual compatibility prediction may be jointly conditioned on item type, context, and style by determining a first compatibility score jointly conditioned on type (e.g., category) and context, determining a second compatibility score conditioned on outfit style, and combining the first and second compatibility scores into a unified visual compatibility score. A unified visual compatibility score may be determined for each of a plurality of candidate items, and the candidate item with the highest unified visual compatibility score may be selected to add to the bundle (e.g., fill the in blank for the partial outfit).

Deep learning based visual compatibility prediction for bundle recommendations

Content creation techniques are described that leverage content analytics to provide insight and guidance as part of content creation. To do so, content features are extracted by a content analytics system from a plurality of content and used by the content analytics system as a basis to generate a content dataset. Event data is also collected by the content analytics system from an event data source. Event data describes user interaction with respective items of content, including subsequent activities in both online and physical environments. The event data is then used to generate an event dataset. An analytics user interface is then generated by the content analytics system using the content dataset and the event dataset and is usable to guide subsequent content creation and editing.

Content analytics as part of content creation

Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.

Form structure similarity detection

Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.

Optimal sequential decision making with changing action space

The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.

Language model with external knowledge base

Growing community of inventors

Noida, India

Balaji Krishnamurthy