Inception Institute of Artificial Intelligence, Ltd.

Artificial Intelligence, Ltd.

Jianbing Shen

Ling Shao

Wenguan Wang

Zhijie Zhang

Hang Dai

Ziyi Shen

Xiankai Lu

Huazhu Fu

This disclosure relates to improved techniques for performing human parsing functions using neural network architectures. The neural network architecture can model human objects in images using a hierarchal graph of interconnected nodes that correspond to anatomical features at various levels. Multi-level inference information can be generated for each of the nodes using separate inference processes. The multi-level inference information for each node can be combined or fused to generate final predictions for each of the nodes. Parsing results may be generated based on the final predictions.

Human parsing techniques utilizing neural network architectures

This disclosure relates to improved techniques for performing computer vision functions including motion deblurring functions. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can include a human-aware attention model that is able to distinguish between foreground human objects and background portions of degraded images affected by motion blur. The neural network architecture further includes an encoder-decoder network that separately performs motion deblurring functions on foreground and background portions of degraded images, and reconstructs enhanced images corresponding to the degraded images.

Motion deblurring using neural network architectures

This disclosure relates to improved techniques for performing image segmentation functions using neural network architectures. The neural network architecture integrates an edge guidance module and object segmentation network into a single framework for detecting target objects and performing segmentation functions. The neural network architecture can be trained to generate edge-attention representations that preserve the edge information included in images. The neural network architecture can be trained to generate multi-scale feature information that preserves and enhances object-level feature information included in images. The edge-attention representations and multi-scale feature information can be fused to generate segmentation results that identify target object boundaries with increased accuracy.

Medical image segmentation using an integrated edge guidance module and object segmentation network

Growing community of inventors

Abu Dhabi, United Arab Emirates

Jianbing Shen