Huawei Technologies Co., Limited

Jin Tang

Yuanhao Yu

Yang Wang

Zhixiang Chi

Juwei Lu

Wentao Liu

Zheng Liu

Rasoul Mohammadi Nasiri

Wei Li

Weiming Duan

Rui Xiang Chai

Irina Kezele

Roel Vertegaal

Fangfu Guo

Ghazaleh Saniee-Monfared

Chenhe Li

Huan Liu

Zhenhao Li

Xiaolin Wu

Huiping Duan

Seel Nimeshkumar Patel

Amartya Mukherjee

Sheral Sweta Kumar

Thu Ha Trinh

Marcello Giordano

Shuhao Li

Seyed Mehdi Ayyoubzadeh

Hisham Elhalabi

Mostafa Shahabinejad

Siwen Yang

Seyed Shahabeddin Nabavi

Zhou Xuan

Li Gu

There is provided a method, apparatus and system for adapting a machine learning model for optical flow prediction. A machine learning model can be trained or adapted based on compressed video data, using motion vector information extracted from the compressed video data as ground-truth information for use in adapting the model to a motion vector prediction task. The model so adapted can accordingly be adapted for the similar task of optical flow prediction. Thus, the model can be adapted at test time to image data which is taken from an appropriate distribution. A meta-learning process can be performed prior to such model adaptation to potentially improve the model's performance.

Method, apparatus and system for adaptating a machine learning model for optical flow map prediction

Methods, devices and computer-readable media for processing a compressed video to perform an inference task are disclosed. Processing the compressed video may include selecting a subset of frame encodings of the compressed video, or zero or more modalities (RGB, motion vectors, residuals) of a frame encoding, for further processing to perform the inference task. Pre-existing motion vector and/or residual information in frame encodings of the compressed video are leveraged to adaptively and efficiently perform the inference task. In some embodiments, the inference task is an action recognition task, such as a human action recognition task.

Method, device, and medium for adaptive inference in compressed video domain

Systems, methods and computer-readable medium for predicting a depth for a video frame are disclosed. An example method may include steps of: receiving a plurality of training data, each comprising a set of consecutive video frames and a depth representation of a subsequent video frame to the consecutive video frames; receiving a pre-trained neural network model fhaving a plurality of weights θ; while the pre-trained neural network model fhas not converged: computing a plurality of second weights θ', based on each set of consecutive video frames, and updating the plurality of weights θ, based on the plurality of training data and the plurality of second weights θ′; receiving a plurality of new consecutive video frames with consecutive timestamps; and predicting a depth representation of video frame immediately subsequent to the new consecutive video frames based on the updated plurality of weights θ.

Methods, systems and computer medium for scene-adaptive future depth prediction in monocular videos

This disclosure provides for methods and system for meta few-shot class incremental learning. According to an aspect a method is provided. The method includes obtaining at least one weight attention map of a first network and updating weights of a second network using the at least one weight attention map, where the second network is a modulatory network. The method further includes generating at least one feature attention map of the second network based on the at least one weight attention map of the first network and a set of input images of at least one class. The method further includes generating at least one feature map of the first network based on the set of input images of the at least one class, and updating the at least one feature map of the first network based on the feature attention map of the second network.

Meta few-shot class incremental learning

Methods and systems for determining a surface color of a target surface under an environment with an environmental light source. A plurality of images of the target surface are captured as the target surface is illuminated with a variable intensity, constant color light source and a constant intensity, constant color environmental light source, wherein the intensity of the light source on the target surface is varied by a known amount between the capturing of the images. A color feature tensor, independent of the environmental light source, is extracted from the image data, and used to infer a surface color of the target surface.

Method, system, and device for color measurement of a surface

A method, device and computer-readable medium for generating a super-resolution version of a compressed video stream. By leveraging the motion information and residual information in compressed video streams, described examples are able to skip the time-consuming motion-estimation step for most frames and make the most use of the SR results of key frames. A key frame SR module generates SR versions of I-frames and other key frames of a compressed video stream using techniques similar to existing multi-frame approaches to VSR. A non-key frame SR module generates SR version of the non-key inter frames between these key frames by making use of motion information and residual information used to encode the inter frames in the compressed video stream.

Method, device, and medium for generating super-resolution video

Methods and systems for image deblurring are described. The weights of a deblurring network are first trained by meta-training the deblurring network on both a primary deblurring task and an auxiliary reconstruction task. Application-time training of the deblurring network is then performed, using an application-time blurry input image, to obtain values of application-time trained weights. Application-time training includes performing the auxiliary reconstruction task on the application-time blurry input image, and updating the weights of the deblurring network based on an auxiliary loss computed from the auxiliary reconstruction task. A deblurred output image is generated from the application-time blurry input image, using the application-time trained weights in the deblurring network.

Methods and systems for deblurring blurry images

Devices and methods for extracting motion vector data during decoding compressed of video data are described. At a video decoder, an encoded video data for a frame of video from an input buffer is obtained. The encoded video data is decoded to obtain decoded image data for a decoded frame, where the decoding includes extracting corresponding motion vector data for the decoded frame. The decoded image data is stored in a temporary storage indexed with a given index, and the corresponding motion vector data is stored in a same or different temporary storage indexed with the given index. An output buffer indexed with the given index is filled with the decoded image data and the corresponding motion vector data stored in the respective temporary storage indexed with the given index.

Methods and devices for extracting motion vector data from compressed video data

A system and a method for performing 2D on-screen user gaze estimation using an input facial image of a user captured using a camera associated to a processing device having a display. The method and system allow automated user calibration through automatic recording of calibration samples each including a calibration facial image of the user and an interaction point corresponding to a point on the display where an occurrence of a user interaction was detected when the corresponding calibration image was captured. The system and method also optimize user-specific parameters using the calibration samples by iteratively minimizing a total difference between the interaction points of a plurality of the calibration samples and corresponding 2D gaze estimation results and convert an estimated 3D gaze direction into a 2D gaze estimation result corresponding to a point on the display, by applying the users-specific parameters.

System, method and storage medium for 2D on-screen user gaze estimation

Methods and devices for generating a slow motion video segment are described. A first set of video frames captures a video view at a first resolution and at a first frame rate. A second set of video frames captures the video view at a second lower resolution, and at a second frame rate that is greater for at least a portion of the second set. At least two high resolution frames are identified in the first set for generating the slow motion video segment. One or more low resolution frames are identified in the second set corresponding to an inter-frame time period between the identified high resolution frames. The slow motion video segment is generated by generating at least one high resolution frame corresponding to the inter-frame time period using interpolation based on the identified high resolution frames and the identified low resolution frames.

Methods and devices for capturing high-speed and high-definition videos

System and method for managing a user interface for a foldable device are disclosed. The method includes: determining a folding state of the foldable device based on a folding angle of the foldable device; detecting an input gesture on one or more touch-sensitive display segments of a flexible display screen of a foldable device; determining a gesture type corresponding to the detected input gesture; determining whether the detected input gesture is a valid gesture or invalid gesture; and responsive to determining that the detected input gesture is a valid gesture, providing the gesture type corresponding to the detected gesture, the folding state of the foldable device, and display information to a software application or an operating system of the foldable device.

Method and system for processing gestures detected on a display screen of a foldable device

Systems and methods for multi-frame video frame interpolation. Higher-order motion modeling, such as cubic motion modeling, achieves predictions of intermediate optical flow between multiple interpolated frames, assisted by relaxation of the constraints imposed by the loss function used in initial optical flow estimation. A temporal pyramidal optical flow refinement module performs coarse-to-fine refinement of the optical flow maps used to generate the intermediate frames, focusing a proportionally greater amount of refinement attention to the optical flow maps for the high-error middle frames. A temporal pyramidal pixel refinement module performs coarse-to-fine refinement of the generated intermediate frames, focusing a proportionally greater amount of refinement attention to the high-error middle frames. A generative adversarial network (GAN) module calculates a loss function for training the neural networks used in the optical flow estimation module, temporal pyramidal optical flow refinement module, and/or temporal pyramidal pixel refinement module.

Systems and methods for multi-frame video frame interpolation

A system, device and method of video frame interpolation. In one aspect, a frame interpolation system receive candidate interpolated frame and difference data based on a first frame and second frame. It identifies a high-error region and one or more increasingly lower-error regions of the candidate interpolated frame based on the difference data and applies multiple successive stages of a refinement neural network to the regions of the candidate interpolated frame, from highest- to lowest-error. At each stage, the results of the previous stage are combined with the next lower-error region before being refined by the current stage. In some aspects, the system also generates the candidate interpolated frame and the difference data using a flow estimation neural network.

System, device and method for video frame interpolation using a structured neural network

The present invention relates to a method for positioning a mobile station and a repeater thereof. Said method comprises: the mobile communication network, when receiving a positioning request from a mobile station, instructing repeaters to send auxiliary positioning signals, and the mobile station performs measurement according to the received auxiliary positioning signals sent from the repeaters and downlink signals sent from the base station, and then estimating the position of the mobile station according to the measurement results, and thereby implementing the positioning of the mobile station. Said repeater is implemented through adding an auxiliary positioning unit in the downlink processing channel of a traditional repeater; said auxiliary positioning unit comprises a communication module, a frame timing recovery module, a timing control module, and a pilot modulating module. The present invention achieves the object of improving the accuracy of positioning the mobile station within the coverage area of repeaters; in addition, the repeater with auxiliary positioning function according to the present invention is simple to be implemented and doesn't affect the structure and signaling flow of the traditional mobile station.

Method for positioning mobile station and repeater thereof

The invention discloses a method of locating and measuring a mobile station, which relates with the radio locating technique in mobile communication field. Comparing with the conventional radio locating technique, the method improves the first path determination method of a neighbor base station downlink signal as follows: with geometrical relationship such as the distance between a MS to the reference base station and the distance between a neighbor base station to the reference base station, an effective range of the downlink signal first path of the neighbor base station can be calculated; the effective range is an effective search window that is shorter than the original large search window, and the first path determination of the neighbor base station downlink signal is made within the effective search window. The invention raises the first path determination accuracy of a neighbor base station downlink signal, so the mobile station locating accuracy is raised too.

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Markham, Canada

Jin Tang