Adobe, Inc.

Zoya Bylinskii

Elya Shechtman

Aaron Phillip Hertzmann

Sumit Shekhar

Bryan Russell

Sanyam Jain

Ritwick Chaudhry

Amish Kumar Bedi

Damanpreet Kaur

Youssef Alami Mejjati

Ayush Goyal

Tushar Gurjar

Tianyuan Cai

Aleena Niklaus

Techniques are disclosed for readability theme generation. The techniques include obtaining reading formats and generating reading format digital images based on the obtained reading formats. The reading format digital images are encoded using a trained machine learning model as perceptual embeddings. The perceptual embeddings are clustered into reading format clusters and readability themes are determined based on the reading format clusters.

Readability theme generation

This disclosure describes methods, systems, and non-transitory computer readable media for automatically parsing infographics into segments corresponding to structured groups or lists and displaying the identified segments or reflowing the segments into various computing tasks. For example, the disclosed systems may utilize a novel infographic grouping taxonomy and annotation system to group elements within infographics. The disclosed systems can train and apply a machine-learning-detection model to generate infographic segments according to the infographic grouping taxonomy. By generating infographic segments, the disclosed systems can facilitate computing tasks, such as converting infographics into digital presentation graphics (e.g., slide carousels), reflow the infographic into query-and-response models, perform search functions, or other computational tasks.

Parsing and reflowing infographics using structured lists and groups

This disclosure includes technologies for image processing, specifically for generating layout variations that are different but visually consistent with the input layout. The disclosed system determines a visual flow of the design blocks in the input layout, and then generates layout variations based on the visual flow. Advantageously, the disclosed technologies enable both novices and seasoned designers to efficiently create alternative layout variations, even in real-time with intricate designs.

Automatic generation of layout variations based on visual flow

Embodiments disclosed herein describe systems, methods, and products that train one or more neural networks and execute the trained neural network across various applications. The one or more neural networks are trained to optimize a loss function comprising a pixel-level comparison between the outputs generated by the neural networks and the ground truth dataset generated from a bubble view methodology or an explicit importance maps methodology. Each of these methodologies may be more efficient than and may closely approximate the more expensive but accurate human eye gaze measurements. The embodiments herein leverage an existing process for training neural networks to generate importance maps of a plurality of graphic objects to offer interactive applications for graphics designs and data visualizations. Based on the importance maps, the computer may provide real-time design feedback, generate smart thumbnails of the graphic objects, provide recommendations for design retargeting, and extract smart color themes from the graphic objects.

Systems and methods of learning visual importance for graphic design and data visualization

Techniques of adjusting the salience of an image include generating values of photographic development parameters for a foreground and background of an image to adjust the salience of the image in the foreground. These parameters are global in nature over the image rather than local. Moreover, the optimization of the salience over such sets of global parameters is provided through two sets of these parameters by an encoder: one set corresponding to the foreground, in which the salience is to be either increased or decreased, and the other set corresponding to the background. Once the set of development parameters corresponding to the foreground region and the set of development parameters corresponding to the background region have been determined, a decoder generates an adjusted image with an increased salience based on these sets of development parameters.

Attention-driven image manipulation

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Cambridge, MA, United States of America

Zoya Bylinskii