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公开(公告)号：US20220392192A1
公开(公告)日：2022-12-08
申请号：US17890020
申请日：2022-08-17
Inventor： Zhigang Wang , Jian Wang , Hao Sun
IPC: G06V10/74 , G06V10/98 , G06V10/764 , G06V40/50 , G06V10/77
Abstract: A target re-recognition method, a target re-recognition device and an electronic device are provided, which relate to the field of artificial intelligence, in particularly to the field of computer vision and deep learning. The target re-recognition method includes obtaining a to-be-recognized image, and the to-be-recognized image including image content of a target object; recognizing first appearance presentation information corresponding to the target object, and the first appearance presentation information being configured to represent a presentation form of an appearance of the target object in the to-be-recognized image; obtaining from a data retrieval library a candidate retrieval image matching the first appearance presentation information; and performing target re-recognition on the to-be-recognized image based on the candidate retrieval image.
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公开(公告)号：US20220391780A1
公开(公告)日：2022-12-08
申请号：US17820758
申请日：2022-08-18
Inventor： Ji LIU , Beichen MA , Chendi ZHOU , Juncheng JIA , Dejing DOU , Shilei JI , Yuan LIAO
Abstract: The present disclosure provides a method of federated learning. A specific implementation solution includes: determining, for a current learning period, a target device for each task of at least one learning task to be performed, from a plurality of candidate devices according to a plurality of resource information of the plurality of candidate devices; transmitting a global model for the each task to the target device for the each task, so that the target device for the each task trains the global model for the each task; and updating, in response to receiving trained models from all target devices for the each task, the global model for the each task according to the trained models, so as to complete the current learning period. The present disclosure further provides an electronic device, and a storage medium.
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公开(公告)号：US20220383876A1
公开(公告)日：2022-12-01
申请号：US17818609
申请日：2022-08-09
Inventor： Yixiang CHEN , Junchao WANG , Yongguo KANG
Abstract: A method of converting a speech, an electronic device, and a readable storage medium are provided, which relate to a field of artificial intelligence technology such as speech and deep learning, in particular to speech converting technology. The method of converting a speech includes: acquiring a first speech of a target speaker; acquiring a speech of an original speaker; extracting a first feature parameter of the first speech of the target speaker; extracting a second feature parameter of the speech of the original speaker; processing the first feature parameter and the second feature parameter to obtain a Mel spectrum information; and converting the Mel spectrum information to output a second speech of the target speaker having a tone identical to a tone of the first speech of the target speaker and a content identical to a content of the speech of the original speaker.
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公开(公告)号：US20220383613A1
公开(公告)日：2022-12-01
申请号：US17818406
申请日：2022-08-09
Inventor： Lele JIA
IPC: G06V10/44 , G06V10/46 , G06V10/72 , G06V10/762
Abstract: The present disclosure provides an object association method and apparatus, and an electronic device, which relate to the technical field of maps. A specific implementation solution is: when performing object association, extracting first description information of each of a plurality of first objects from real data, and extracting second description information of each of a plurality of second objects from high-definition map data; and determining, according to the first description information and the second description information, association probabilities between the first objects and the second objects; then determining, according to the association probabilities between the first objects and the second objects, an association result of the first objects and the second objects, thus realizing automatic associations between objects in real world and objects in a high-definition map, and improving an association efficiency of objects.
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495.发明申请公开(公告)号：US20220383535A1
公开(公告)日：2022-12-01
申请号：US17776155
申请日：2020-09-25
Inventor： Xiangbo SU , Yuchen YUAN , Hao SUN
Abstract: The present disclosure provides an object tracking method, an object tracking device, an electronic device and a computer-readable storage medium, and relates to the field of computer vision technology. The object tracking method includes: detecting an object in a current image, so as to obtain first information about an object detection box, the first information being used to indicate a first position and a first size; tracking the object through a Kalman filter, so as to obtain second information about an object tracking box in the current image, the second information being used to indicate a second position and a second size; performing fault-tolerant modification on a predicted error covariance matrix in the Kalman filter, so as to obtain a modified covariance matrix; calculating a Mahalanobis distance between the object detection box and the object tracking box in the current image in accordance with the first information, the second information and the modified covariance matrix; and performing a matching operation between the object detection box and the object tracking box in the current image in accordance with the Mahalanobis distance.
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Patent Agency Ranking
496.发明申请公开(公告)号：US20220374776A1
公开(公告)日：2022-11-24
申请号：US17868113
申请日：2022-07-19
Inventor： Ji LIU , Beichen MA , Chendi ZHOU , Jingbo ZHOU , Ruipu ZHOU , Dejing DOU
IPC: G06N20/00
Abstract: The present disclosure provides a method and apparatus for federated learning, which relate to the technical fields such as big data and deep learning. A specific implementation is: generating, for each task in a plurality of different tasks trained simultaneously, a global model for each task; receiving resource information of each available terminal in a current available terminal set; selecting a target terminal corresponding to each task from the current available terminal set, based on the resource information and the global model; and training the global model using the target terminal until a trained global model for each task meets a preset condition.
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公开(公告)号：US20220374775A1
公开(公告)日：2022-11-24
申请号：US17867516
申请日：2022-07-18
Inventor： Ji LIU , Beichen MA , Jingbo ZHOU , Ruipu ZHOU , Dejing DOU
Abstract: A method for multi-task scheduling, a device and a storage medium are provided. The method may include: initializing a list of candidate scheduling schemes, the candidate scheduling scheme being used to allocate a terminal device for training to each machine learning task in a plurality of machine learning tasks; perturbing, for each candidate scheduling scheme in the list of candidate scheduling schemes, the candidate scheduling scheme to generate a new scheduling scheme; determining whether to replace the candidate scheduling scheme with the new scheduling scheme based on a fitness value of the candidate scheduling scheme and a fitness value of the new scheduling scheme, to generate a new scheduling scheme list; and determining a target scheduling scheme, based on the fitness value of each new scheduling scheme in the new scheduling scheme list.
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公开(公告)号：US20220374742A1
公开(公告)日：2022-11-24
申请号：US17817015
申请日：2022-08-03
Inventor： Zhengxiong Yuan , Zhengyu Qian , En Shi , Mingren Hu , Jinqi Li , Zhenfang Chu , Runqing Li , Yue Huang
Abstract: A method for running an inference service platform, includes: determining inference tasks to be allocated for the inference service platform, in which the inference service platform includes two or more inference service groups, versions of the inference service groups are different, and the inference service groups are configured to perform a same type of inference services; determining a flow weight of each of the inference service groups, in which the flow weight is configured to indicate a proportion of a number of inference tasks to which the corresponding inference service group need to be allocated in a total number of inference tasks; and allocating the corresponding number of inference tasks in the inference tasks to be allocated to each of the inference service groups based on the flow weight of each of the inference service groups; and performing the inference tasks by the inference service group.
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499.发明申请公开(公告)号：US20220374704A1
公开(公告)日：2022-11-24
申请号：US17558355
申请日：2021-12-21
Inventor： Danlei FENG , Long LIAN , Dianhai YU , Xuefeng YAO , Xinxuan WU , Zhihua WU , Yanjun MA
Abstract: The disclosure provides a neural network training method and apparatus, an electronic device, a medium and a program product, and relates to the field of artificial intelligence, in particular to the fields of deep learning and distributed learning. The method includes: acquiring a neural network for deep learning; constructing a deep reinforcement learning model for the neural network; and determining, through the deep reinforcement learning model, a processing unit selection for the plurality of the network layers based on a duration for training each of the network layers by each type of the plurality of types of the processing units, and a cost of each type of the plurality of types of the processing units, wherein the processing unit selection comprises the type of the processing unit to be used for each of the plurality of the network layers, and the processing unit selection is used for making a total cost of the processing units used by the neural network below a cost threshold, in response to a duration for pipelining parallel computing for training the neural network being shorter than a present duration.
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公开(公告)号：US20220366320A1
公开(公告)日：2022-11-17
申请号：US17864098
申请日：2022-07-13
Inventor： Ji LIU , Chendi ZHOU , Juncheng JIA , Dejing DOU
IPC: G06N20/20
Abstract: A computer-implemented method is provided. The method includes: executing, for each task in a federated learning system, a first training process comprising: obtaining resource information of a plurality of terminal devices of the federated learning system; determining one or more target terminal devices corresponding to the task based on the resource information; and training a global model corresponding to the task by the target terminal devices until the global model meets a preset condition.
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