Adobe, Inc.

Chenglin Yang

Zhe Lin

Jianming Zhang

Yilin Wang

He Zhang

Zijun Wei

Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing a convolutional self-attention operation that outputs a plurality of attention-weighted values for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.

Transformer for efficient image segmentation

Systems and methods for image segmentation are described. Embodiments of the present disclosure receive an image depicting an object; generate image features for the image by performing an atrous self-attention operation based on a plurality of dilation rates for a convolutional kernel applied at a position of a sliding window on the image; and generate label data that identifies the object based on the image features.

The present disclosure relates to systems, methods, and non-transitory computer-readable media that utilizes a neural network having a hierarchy of hierarchical point-wise refining blocks to generate refined segmentation masks for high-resolution digital visual media items. For example, in one or more embodiments, the disclosed systems utilize a segmentation refinement neural network having an encoder and a recursive decoder to generate the refined segmentation masks. The recursive decoder includes a deconvolution branch for generating feature maps and a refinement branch for generating and refining segmentation masks. In particular, in some cases, the refinement branch includes a hierarchy of hierarchical point-wise refining blocks that recursively refine a segmentation mask generated for a digital visual media item. In some cases, the disclosed systems utilize a segmentation refinement neural network that includes a low-resolution network and a high-resolution network, each including an encoder and a recursive decoder, to generate the refined segmentation masks.

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Towson, MD, United States of America

Chenglin Yang