Search Results for author: Yingying Wang

Found 8 papers, 2 papers with code

3D Hand Shape and Pose Estimation from a Single RGB Image

2 code implementations CVPR 2019 Liuhao Ge, Zhou Ren, Yuncheng Li, Zehao Xue, Yingying Wang, Jianfei Cai, Junsong Yuan

This work addresses a novel and challenging problem of estimating the full 3D hand shape and pose from a single RGB image.

3D Hand Pose Estimation

YACLC: A Chinese Learner Corpus with Multidimensional Annotation

1 code implementation30 Dec 2021 Yingying Wang, Cunliang Kong, Liner Yang, Yijun Wang, Xiaorong Lu, Renfen Hu, Shan He, Zhenghao Liu, Yun Chen, Erhong Yang, Maosong Sun

This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.

Grammatical Error Correction Language Acquisition +1

A Multimodal Motion-Captured Corpus of Matched and Mismatched Extravert-Introvert Conversational Pairs

no code implementations LREC 2016 Jackson Tolins, Kris Liu, Yingying Wang, Jean E. Fox Tree, Marilyn Walker, Michael Neff

This paper presents a new corpus, the Personality Dyads Corpus, consisting of multimodal data for three conversations between three personality-matched, two-person dyads (a total of 9 separate dialogues).

Boundary Content Graph Neural Network for Temporal Action Proposal Generation

no code implementations ECCV 2020 Yueran Bai, Yingying Wang, Yunhai Tong, Yang Yang, Qiyue Liu, Junhui Liu

To address this issue, we propose a novel Boundary Content Graph Neural Network (BC-GNN) to model the insightful relations between the boundary and action content of temporal proposals by the graph neural networks.

Action Detection Action Understanding +1

DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning

no code implementations18 Feb 2024 Yingying Wang, Yun Xiong, Xixi Wu, Xiangguo Sun, Jiawei Zhang

(2) the scarcity of labeled data for rare events, which is a pervasive issue in the medical field where rare yet potentially critical interactions are often overlooked or under-studied due to limited available data.

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