Samsung Electronics Co., Ltd.

Haoyu Ren

Jungwon Lee

Mostafa El-Khamy

Aman Raj

Dongwoon Bai

Yoo Jin Choi

Shuangquan Wang

Xianzhi Du

Mostafa El Khamy

Amin Kheradmand

A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

System and method for deep machine learning for computer vision applications

A method of depth detection based on a plurality of video frames includes receiving a plurality of input frames including a first input frame, a second input frame, and a third input frame respectively corresponding to different capture times, convolving the first to third input frames to generate a first feature map, a second feature map, and a third feature map corresponding to the different capture times, calculating a temporal attention map based on the first to third feature maps, the temporal attention map including a plurality of weights corresponding to different pairs of feature maps from among the first to third feature maps, each weight of the plurality of weights indicating a similarity level of a corresponding pair of feature maps, and applying the temporal attention map to the first to third feature maps to generate a feature map with temporal attention.

Video depth estimation based on temporal attention

Apparatuses and methods of manufacturing same, systems, and methods for generating a convolutional neural network (CNN) are described. In one aspect, a minimal CNN having, e.g., three or more layers is trained. Cascade training may be performed on the trained CNN to insert one or more intermediate layers until a training error is less than a threshold. When cascade training is complete, cascade network trimming of the CNN output from the cascade training may be performed to improve computational efficiency. To further reduce network parameters, convolutional filters may be replaced with dilated convolutional filters with the same receptive field, followed by additional training/fine-tuning.

System and method for designing efficient super resolution deep convolutional neural networks by cascade network training, cascade network trimming, and dilated convolutions

Method and systems are provided for robust disparity estimation based on cost-volume attention. A method includes extracting first feature maps from left images captured by a first camera; extracting second feature maps from right images captured by a second camera; calculating a matching cost based on a comparison of the first and second feature maps to generate a cost volume; generating an attention-aware cost volume from the generated cost volume; and aggregating the attention-aware cost volume to generate an output disparity.

System and method for disparity estimation based on cost-volume attention

A method and apparatus are provided. The method includes generating a dataset for real-world super resolution (SR), training a first generative adversarial network (GAN), training a second GAN, and fusing an output of the first GAN and an output of the second GAN.

Systems and method of training networks for real-world super resolution with unknown degradations

A method and apparatus for providing a rotational invariant neural network is herein disclosed. According to one embodiment, a method includes receiving a first input of an image in a first orientation and training a kernel to be symmetric such that an output corresponding to the first input is the same as an output corresponding to a second input of the image in a second orientation.

Method and apparatus for providing rotational invariant neural networks

In a method for super resolution imaging, the method includes: receiving, by a processor, a low resolution image; generating, by the processor, an intermediate high resolution image having an improved resolution compared to the low resolution image; generating, by the processor, a final high resolution image based on the intermediate high resolution image and the low resolution image; and transmitting, by the processor, the final high resolution image to a display device for display thereby.

System and method for deep learning image super resolution

A method and an apparatus are provided. The method includes receiving a video with a first plurality of frames having a first resolution; generating a plurality of warped frames from the first plurality of frames based on a first type of motion compensation; generating a second plurality of frames having a second resolution, wherein the second resolution is of higher resolution than the first resolution, wherein each of the second plurality of frames having the second resolution is derived from a subset of the plurality of warped frames using a convolutional network; and generating a third plurality of frames having the second resolution based on a second type of motion compensation, wherein each of the third plurality of frames having the second resolution is derived from a fusing a subset of the second plurality of frames.

Method and apparatus for video super resolution using convolutional neural network with two-stage motion compensation

Apparatuses and methods of manufacturing same, systems, and methods are described. In one aspect, a method includes generating a convolutional neural network (CNN) by training a CNN having three or more convolutional layers, and performing cascade training on the trained CNN. The cascade training includes an iterative process of one or more stages, in which each stage includes inserting a residual block (ResBlock) including at least two additional convolutional layers and training the CNN with the inserted ResBlock.

A method and system for determining depth information of an image are herein provided. According to one embodiment, the method includes receiving an image input, classifying the input image into a depth range of a plurality of depth ranges, and determining a depth map of the image by applying depth estimation based on the depth range into which the input image is classified.

System and method for providing single image depth estimation based on deep neural network

An electronic device and method are herein disclosed. The electronic device includes a first camera with a first field of view (FOV), a second camera with a second FOV that is narrower than the first FOV, and a processor configured to capture a first image with the first camera, the first image having a union FOV, capture a second image with the second camera, determine an overlapping FOV between the first image and the second image, generate a disparity estimate based on the overlapping FOV, generate a union FOV disparity estimate, and merge the union FOV disparity estimate with the overlapping FOV disparity estimate.

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San Diego, CA, United States of America

Haoyu Ren