Beijing Sensetime Technology Development Co., Ltd.

Vmware, Inc.

The Chinese University of Hong Kong

Omnissa, LLC

Sensetime Group Limited

Hongsheng Li

Xiaogang Wang

Dapeng Chen

Shuai Yi

Wanli Ouyang

Tong Xiao

Yantao Shen

Yixiao Ge

Bo Liu

Zhe Wang

Wei Yang

Shih-Chi Chen

Xing Wei

Yuping Wei

Jianping Shi

Huanhuan Zhang

Ke Xiao

Chenglin Li

Xiao Chu

Wenshuo Chen

An Min Gong

Jing Shao

Pan Lu

Dongyu Zhao

Xihui Liu

Zihao Wang

XiangBo Liu

Zhiqiang Hu

Rui Huang

Yunhe Gao

A system is described for redirecting multimedia in a collaborative session on a virtual desktop. The virtual desktop session can be established, and collaborator virtual desktop clients can be connected in a collaborative session where each collaborator can view the desktop GUI in their respective virtual desktop client. A request can be received to play media in a media player in the virtual desktop. The media stream can be intercepted in the virtual desktop before it is rendered in the media player and conveyed to each collaborator's client over a separate virtual channel established between the virtual desktop and each collaborator. The data stream can then be rendered in a client media player by each collaborator's client.

Multimedia redirection in collaborative sessions on virtual desktops

The disclosure provides an image processing method and device, a computer-readable storage medium. The method includes: performing feature extraction on an image to be processed through a primary image segmentation model to obtain a feature map of the image to be processed; and performing image segmentation on a segmentation target according to the feature map through a target image segmentation model to obtain a target segmentation result, herein the target image segmentation model is obtained from an adversarial training with an image encoding model, and the image encoding model is used for placing a shape constraint on the target segmentation result. The disclosure may improve an accuracy of the image segmentation.

Image processing method and device, and computer-readable storage medium

In some embodiments, a control system, a control method and a storage medium are provided. In the method, first motion information of a machine acquired by a first sensor is received; the first motion information is inputted into a deep learning model to obtain a model output, the deep learning model comprising a convolutional neural network (CNN) and a long short-term memory (LSTM); the deep learning model is trained using the first motion information and second motion information acquired by a second sensor; the first sensor and the second sensor having different ways of detecting information and processing the detected information. The model output is used to control the machine.

Control system, control method and computer storage medium

A method and apparatus for positioning a description statement in an image includes: analyzing a to-be-analyzed description statement and a to-be-analyzed image to obtain a plurality of statement attention weights of the to-be-analyzed description statement and a plurality of image attention weights of the to-be-analyzed image; obtaining a plurality of first matching scores based on the plurality of statement attention weights and a subject feature, a location feature and a relationship feature of the to-be-analyzed image; obtaining a second matching score between the to-be-analyzed description statement and the to-be-analyzed image based on the plurality of first matching scores and the plurality of image attention weights; and determining a positioning result of the to-be-analyzed description statement in the to-be-analyzed image based on the second matching score.

Method and apparatus for positioning description statement in image, electronic device, and storage medium

The present disclosure discloses an image processing method and related device thereof. The method includes: acquiring an image to be processed; and performing a feature extraction process on the image to be processed using a target neural network so as to obtain target feature data of the image to be processed, wherein parameters of the target neural network are time average values of parameters of a first neural network which is obtained from training under supervision by a training image set and an average network, and parameters of the average network are time average values of parameters of a second neural network which is obtained from training under supervision by the training image set and the target neural network. A corresponding device is also disclosed. Feature data of image to be processed are obtained via the feature extraction process performed on the image to be processed.

Image processing method, image processing device, and storage medium

The present disclosure relates to a network optimization method and apparatus, an image processing method and apparatus, and a storage medium. The network optimization method includes: obtaining an image sample group; obtaining a first feature and a second feature of an image in the image sample group, and obtaining a first classification result by using the first feature of the image; performing feature exchange processing on an image pair in the image sample group to obtain a new image pair; obtaining a first loss value of the first classification result, a second loss value of the new image pair, and a third loss value of first features and second features of the new image pair in a preset manner; and adjusting parameters of a neural network at least according to the first loss value, the second loss value, and the third loss value until a preset requirement is met.

Network optimization method and apparatus, image processing method and apparatus, and storage medium

A pedestrian re-identification method includes: obtaining a target video containing a target pedestrian and at least one candidate video; encoding each target video segment in the target video and each candidate video segment in the at least one candidate segment separately; determining a score of similarity between the each target video segment and the each candidate video segment according to encoding results, the score of similarity being used for representing a degree of similarity between pedestrian features in the target video segment and the candidate video segment; and performing pedestrian re-identification on the at least one candidate video according to the score of similarity.

Pedestrian re-identification methods and apparatuses, electronic devices, and storage media

An image question answering method includes: extracting a question feature representing a semantic meaning of a question, a global feature of an image, and a detection frame feature of a detection frame encircling an object in the image; obtaining a first weight of each of at least one area of the image and a second weight of each of at least one detection frame of the image according to question feature, global feature, and detection frame feature; performing weighting processing on global feature by using first weight to obtain an area attention feature of image; performing weighting processing on detection frame feature by using second weight to obtain a detection frame attention feature of image; and predicting an answer to question according to question feature, area attention feature, and detection frame attention feature.

Image question answering method, apparatus and system, and storage medium

Image detection methods and apparatuses, electronic devices and storage media include: down-sampling a second image to obtain a first image; determining at least one key sub-region in the second image for category detection; respectively extracting a feature of the first image and a feature of the at least one key sub-region; and generating a category detection result of the second image at least based on the extracted features.

Methods and apparatuses for image detection, electronic devices and storage media

A method for identifying a target, a non-transitory computer-readable storage medium, and an electronic device include: acquiring a first image and a second image, the first image and the second image each including a target to be determined; generating a prediction path based on the first image and the second image, both ends of the prediction path respectively corresponding to the first image and the second image; and performing validity determination on the prediction path and determining, according to a determination result, whether the targets to be determined in the first image and the second image are the same target to be determined.

Target recognition method and apparatus, storage medium, and electronic device

Various examples for providing multiple instances of a client application in operating systems that limit execution of the client application to a single process are disclosed. A client device can include an operating system natively configured to generate a single process for an execution of the client application on the client device. A client application can be configured to, in the single process, generate sub-processes for execution of separate instances of the client application. The client application can include at least one user interface that permits creation of, termination of, or toggling between various instances of the client application.

Multiple application instances in operating systems that utilize a single process for application execution

A method and a system for pose estimation are provided. The method includes: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

Method and system for pose estimation

A method for predicting walking behaviors includes: encoding walking behavior information of at least one target object in a target scene within a historical time period M to obtain a first offset matrix for representing the walking behavior information of the at least one target object within the historical time period M; inputting the first offset matrix into a neural network, and outputting by the neural network a second offset matrix for representing walking behavior information of the at least one target object within a future time period M'; and decoding the second offset matrix to obtain the walking behavior prediction information of the at least one target object within the future time period M&#x2032;.

Method and apparatus for predicting walking behaviors, data processing apparatus, and electronic device

The present application relates to post estimation methods and apparatuses, and computer systems, wherein the method includes: extracting a feature in each body part of body parts of an object in an image to generate feature maps for the each body part, wherein the each body part corresponds to at least one part type; predicting part type scores respectively for features in each of the feature maps and establishing part type score maps based on the predicted part type scores; optimizing, for at least one point in each of the part type score maps, the established part type score maps based on a message related to an adjacent body part of a body part corresponding to the point; and determining an estimated position and an estimated part type of the each body part based on the optimized part type score maps to obtain an estimated pose of the object.

Growing community of inventors

Beijing, China

Hongsheng Li