公开(公告)号：US20240370719A1

公开(公告)日：2024-11-07

申请号：US18512766

申请日：2023-11-17

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Congxi XIAO ,  Jizhou HUANG ,  Jingbo ZHOU

IPC: G06N3/08 ,  G06N3/042

Abstract: This disclosure provides a data generation method, model training method, electronic device, and medium. The data generation method includes: obtaining urban graph data, the urban graph data including a node set, an edge set and a feature set, wherein the node set includes a central node corresponding to a predetermined urban entity, the edge set includes a neighborhood corresponding to the central node, the neighborhood includes other nodes in the node set connected to the central node via an edge, and the feature set includes features of nodes in the node set; partitioning a target region into at least two sub-regions to obtain a region partition set; obtaining a regional feature of each sub-region by aggregating features corresponding to all nodes in the sub-region; and updating a feature of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.

公开(公告)号：US20240282103A1

公开(公告)日：2024-08-22

申请号：US18654477

申请日：2024-05-03

Applicant: Beijing Baidu Netcom Science Technology Co., Ltd.

Inventor： Congxi XIAO ,  Jizhou HUANG ,  Jingbo ZHOU

IPC: G06V20/10 ,  G06V10/26 ,  G06V10/74 ,  G06V10/82

CPC classification number: G06V20/176 ,  G06V10/26 ,  G06V10/761 ,  G06V10/82

Abstract: A data updating method, a model training method and related devices are provided. The method includes obtaining urban graph data in a preset region, the urban graph data including a node set including central nodes, an edge set and a feature set, the edge set including neighborhoods corresponding to the central nodes, the neighborhoods including other nodes possessing connecting edges with the central nodes, the neighborhoods corresponding to a target region, and the feature set including node features of the nodes in the node set; partitioning the target region into at least two sub-regions to obtain a region partition set; aggregating the node features corresponding to all nodes located within the same sub-region to obtain the regional features of each of the sub-regions; updating the node features of the central node based on the regional features of the sub-regions in the region partition set to obtain target feature data.

公开(公告)号：US20250139369A1

公开(公告)日：2025-05-01

申请号：US18956107

申请日：2024-11-22

Applicant: BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD.

Inventor： Jingbo ZHOU ,  Congxi XIAO ,  Yixiong XIAO ,  Jizhou HUANG

IPC: G06F40/284 ,  G06N20/00

Abstract: A method is provided that includes: obtaining first urban data of a first sample urban region; inputting the first urban data into a multi-modal foundation model to obtain respective predicted vector representations of a plurality of first data segments; obtaining a plurality of general-purpose foundation models that are pre-trained; for each general-purpose foundation model: generating a vector representation label of a first data segment of a corresponding data modality by using the general-purpose foundation model; and determining a knowledge distillation loss of the general-purpose foundation model based on the vector representation label and a predicted vector representation of the first data segment; and adjusting parameters of the multi-modal foundation model based on at least respective knowledge distillation losses of the plurality of general-purpose foundation models.