Abstract: This disclosure relates to improved techniques for performing image segmentation functions using neural network architectures. The neural network architecture can include an attentive graph neural network (AGNN) that facilitates performance of unsupervised video object segmentation (UVOS) functions and image object co-segmentation (IOCS) functions. The AGNN can generate a graph that utilizes nodes to represent images (e.g., video frames) and edges to represent relations between the images. A message passing function can propagate messages among the nodes to capture high-order relationship information among the images, thus providing a more global view of the video or image content. The high-order relationship information can be utilized to more accurately perform UVOS and/or IOCS functions.

Abstract: 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.