Search Results for author: Xingchen Li

Found 12 papers, 3 papers with code

A Joint Model for Graph-based Chinese Dependency Parsing

no code implementations CCL 2020 Xingchen Li, Mingtong Liu, Yujie Zhang, Jinan Xu, Yufeng Chen

The experimental results on the Penn Chinese treebank (CTB5) show that our proposed joint model improved by 0. 38% on dependency parsing than the model of Yan et al. (2019).

Chinese Dependency Parsing Chinese Word Segmentation +5

EdgeCalib: Multi-Frame Weighted Edge Features for Automatic Targetless LiDAR-Camera Calibration

no code implementations25 Oct 2023 Xingchen Li, Yifan Duan, Beibei Wang, Haojie Ren, Guoliang You, Yu Sheng, Jianmin Ji, Yanyong Zhang

The edge features, which are prevalent in various environments, are aligned in both images and point clouds to determine the extrinsic parameters.

Camera Calibration

USTC FLICAR: A Sensors Fusion Dataset of LiDAR-Inertial-Camera for Heavy-duty Autonomous Aerial Work Robots

no code implementations4 Apr 2023 ZiMing Wang, Yujiang Liu, Yifan Duan, Xingchen Li, Xinran Zhang, Jianmin Ji, Erbao Dong, Yanyong Zhang

In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots.

3D Reconstruction Autonomous Driving +2

Standoff Tracking Using DNN-Based MPC with Implementation on FPGA

no code implementations21 Dec 2022 Fei Dong, Xingchen Li, Keyou You, Shiji Song

This work studies the standoff tracking problem to drive an unmanned aerial vehicle (UAV) to slide on a desired circle over a moving target at a constant height.

Model Predictive Control Trajectory Planning +1

Integrating Object-aware and Interaction-aware Knowledge for Weakly Supervised Scene Graph Generation

1 code implementation3 Aug 2022 Xingchen Li, Long Chen, Wenbo Ma, Yi Yang, Jun Xiao

However, we argue that most existing WSSGG works only focus on object-consistency, which means the grounded regions should have the same object category label as text entities.

Graph Generation Object +1

Rethinking the Evaluation of Unbiased Scene Graph Generation

no code implementations3 Aug 2022 Xingchen Li, Long Chen, Jian Shao, Shaoning Xiao, Songyang Zhang, Jun Xiao

Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates.

Graph Generation Unbiased Scene Graph Generation

A deep learning method based on patchwise training for reconstructing temperature field

no code implementations26 Jan 2022 Xingwen Peng, Xingchen Li, Zhiqiang Gong, Xiaoyu Zhao, Wen Yao

To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation.

Management

Unified Style Transfer

1 code implementation20 Oct 2021 Guanjie Huang, Hongjian He, Xiang Li, Xingchen Li, Ziang Liu

Currently, it is hard to compare and evaluate different style transfer algorithms due to chaotic definitions of style and the absence of agreed objective validation methods in the study of style transfer.

Philosophy Style Transfer +1

An Infrared Communication System based on Handstand Pendulum

no code implementations9 Sep 2020 Xingchen Li, Changlu Li, Yun Wang, Mengqi Lei

In this system, 940nm infrared light is mainly used for audio signal transmission, and an handstand pendulum based on PID is used to control the angle and stability of infrared light emission.

Hierarchical Fashion Graph Network for Personalized Outfit Recommendation

1 code implementation26 May 2020 Xingchen Li, Xiang Wang, Xiangnan He, Long Chen, Jun Xiao, Tat-Seng Chua

Fashion outfit recommendation has attracted increasing attentions from online shopping services and fashion communities. Distinct from other scenarios (e. g., social networking or content sharing) which recommend a single item (e. g., a friend or picture) to a user, outfit recommendation predicts user preference on a set of well-matched fashion items. Hence, performing high-quality personalized outfit recommendation should satisfy two requirements -- 1) the nice compatibility of fashion items and 2) the consistence with user preference.

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