Adobe, Inc.

Bhanu Prakash Reddy Guda

Vinay Aggarwal

Balaji Vasan Srinivasan

Niyati Himanshu Chhaya

Vishwa Vinay

Sumit Shekhar

Harshit Agrawal

Prabhat Mahapatra

Rizurekh Saha

Chloe McConnell

Vlad Ion Morariu

Ashutosh Mehra

Aparna Garimella

Ashutosh Chaubey

Ishan Jindal

Avneet Jain

Systems and methods for training an object detection network are described. Embodiments train an object detection network using a labeled training set, wherein each element of the labeled training set includes an image and ground truth labels for object instances in the image, predict annotation data for a candidate set of unlabeled data using the object detection network, select a sample image from the candidate set using a policy network, generate a labeled sample based on the selected sample image and the annotation data, wherein the labeled sample includes labels for a plurality of object instances in the sample image, and perform additional training on the object detection network based at least in part on the labeled sample.

Optimized policy-based active learning for content detection

Techniques for template generation from image content include extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image. A user-editable template having the characteristics of the input image is generated based on the layout information and the text attributes.

Semantically-guided template generation from image content

Embodiments provide systems, methods, and computer storage media for extracting semantic labels for field widgets of form fields in unfilled forms. In some embodiments, a processing device accesses a representation of a fillable widget of a form field of an unfilled form. The processing device generates an encoded input representing text and layout of a sequence of tokens in a neighborhood of the fillable widget. The processing device uses a machine learning model to extract a semantic label representing a field type of the fillable widget in view of the encoded input. The processing device causes execution of an action using the semantic label.

Automatic semantic labeling of form fields with limited annotations

Techniques for template generation from image content includes extracting information associated with an input image. The information comprises: 1) layout information indicating positions of content corresponding to a content type of a plurality of content types within the input image; and 2) text attributes indicating at least a font of text included in the input image. A user-editable template having the characteristics of the input image is generated based on the layout information and the text attributes.

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Ongole, India

Bhanu Prakash Reddy Guda