Adobe, Inc.

Ruppesh Nalwaya

Hailin Jin

Joon-Young Lee

Bryan Russell

Markus Woodson

Ram Bhushan Agrawal

Prasenjit Mondal

Anuj Shara

Deepanshu Arora

Systems, methods, and non-transitory computer-readable media are disclosed for automatic tagging of videos. In particular, in one or more embodiments, the disclosed systems generate a set of tagged feature vectors (e.g., tagged feature vectors based on action-rich digital videos) to utilize to generate tags for an input digital video. For instance, the disclosed systems can extract a set of frames for the input digital video and generate feature vectors from the set of frames. In some embodiments, the disclosed systems generate aggregated feature vectors from the feature vectors. Furthermore, the disclosed systems can utilize the feature vectors (or aggregated feature vectors) to identify similar tagged feature vectors from the set of tagged feature vectors. Additionally, the disclosed systems can generate a set of tags for the input digital videos by aggregating one or more tags corresponding to identified similar tagged feature vectors.

Generating action tags for digital videos

Generating tags for a digital video

Techniques are disclosed for generating a shadow map of a digital image. In some examples, a method may include generating a shadow mask of a digital image, generating a dilated de-noised binarized gradient image based on the shadow mask, generating a binarized median-filtered gray image based on the digital image and the dilated de-noised binarized gradient image, and generating a shadow map based on the shadow mask and the binarized median-filtered gray image. The generated shadow map can then be used to remove shadows from the digital image without degrading the quality of the image content in the digital image.

Removal of shadows from document images while preserving fidelity of image contents

Growing community of inventors

Sunnyvale, CA, United States of America

Ruppesh Nalwaya