公开(公告)号：US11934887B1

公开(公告)日：2024-03-19

申请号：US18466384

申请日：2023-09-13

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Fei Wu ,  Guang Chen ,  Feng Lin

IPC: G06F9/50 ,  G06F8/41

CPC classification number: G06F9/5072 ,  G06F8/41 ,  G06F9/5066 ,  G06F2209/5016 ,  G06F2209/5017

Abstract: The present disclosure discloses a distributed model compilation system. A master node of the system determines the logic calculation graph of the model based on model information, divides the logic calculation graph into multiple logic calculation sub-graphs, generates a distributing message for each logic calculation sub-graph, and then transmits the distributing message to a slave node. Each of the slave nodes allocates a local computing resource to compile the logic calculation sub-graph based on the received distributing message, and transmits compilation completion information to the master node. The master node determines the completion of model compilation based on the compilation completion information returned by each slave node, and executes the target work based on the compiled model.

公开(公告)号：US11810366B1

公开(公告)日：2023-11-07

申请号：US18072002

申请日：2022-11-30

Applicant: ZHEJIANG LAB

Inventor： Hongsheng Wang ,  Guang Chen

IPC: G06V20/58 ,  G06V10/82

CPC classification number: G06V20/58 ,  G06V10/82

Abstract: Disclosed are a joint modeling method and apparatus for enhancing local features of pedestrians. The method includes the following steps: S1: acquiring an original surveillance video image data set, dividing the original surveillance video image data set into a training set and a test set in proportion; S2: cutting the surveillance video image training set to obtain image block vector sequences. In the present disclosure, local features of pedestrians in video images are extracted by a multi-head attention neural network, weight parameters of image channels are learned by channel convolution kernels, spatial features on the images are scanned through spatial convolution, local features of pedestrians are enhanced to improve the recognition rate of pedestrians, a feed-forward neural network and an activation function are adopted, so as to realize pedestrian re-recognition, thereby obtaining face images available.