no code implementations • ECCV 2020 • Yujun Cai, Lin Huang, Yiwei Wang, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Xu Yang, Yiheng Zhu, Xiaohui Shen, Ding Liu, Jing Liu, Nadia Magnenat Thalmann
Last, in order to incorporate a general motion space for high-quality prediction, we build a memory-based dictionary, which aims to preserve the global motion patterns in training data to guide the predictions.
no code implementations • 31 Mar 2024 • Haoxuan Qu, Yujun Cai, Jun Liu
Motivated by this, we propose a novel LLM-AR framework, in which we investigate treating the Large Language Model as an Action Recognizer.
no code implementations • 29 Dec 2023 • Li Xu, Haoxuan Qu, Yujun Cai, Jun Liu
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
1 code implementation • 7 Nov 2023 • Yihong Luo, Siya Qiu, Xingjian Tao, Yujun Cai, Jing Tang
To address these issues, we introduce a conditional EBM for calibrating the generative direction of VAE during training, without requiring it for the generation at test time.
1 code implementation • 20 Oct 2023 • Yiwei Wang, Yujun Cai, Muhao Chen, Yuxuan Liang, Bryan Hooi
We have two main findings: i) ChatGPT's decision is sensitive to the order of labels in the prompt; ii) ChatGPT has a clearly higher chance to select the labels at earlier positions as the answer.
1 code implementation • NeurIPS 2023 • Haoxuan Qu, Xiaofei Hui, Yujun Cai, Jun Liu
Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not.
1 code implementation • 22 May 2023 • Yiwei Wang, Bryan Hooi, Fei Wang, Yujun Cai, Yuxuan Liang, Wenxuan Zhou, Jing Tang, Manjuan Duan, Muhao Chen
In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context.
1 code implementation • ICCV 2023 • Julian Tanke, Linguang Zhang, Amy Zhao, Chengcheng Tang, Yujun Cai, Lezi Wang, Po-Chen Wu, Juergen Gall, Cem Keskin
We propose Social Diffusion, a novel method for short-term and long-term forecasting of the motion of multiple persons as well as their social interactions.
no code implementations • CVPR 2023 • Haoxuan Qu, Yujun Cai, Lin Geng Foo, Ajay Kumar, Jun Liu
Therefore, via minimizing the distance between the two characteristic functions, we can optimize the model to provide a more accurate localization result for the body joints in different sub-regions of the predicted heatmap.
no code implementations • 31 Oct 2022 • Shangchen Han, Po-Chen Wu, Yubo Zhang, Beibei Liu, Linguang Zhang, Zheng Wang, Weiguang Si, Peizhao Zhang, Yujun Cai, Tomas Hodan, Randi Cabezas, Luan Tran, Muzaffer Akbay, Tsz-Ho Yu, Cem Keskin, Robert Wang
In this paper, we present a unified end-to-end differentiable framework for multi-view, multi-frame hand tracking that directly predicts 3D hand pose in world space.
no code implementations • 3 Oct 2022 • Haoxuan Qu, Li Xu, Yujun Cai, Lin Geng Foo, Jun Liu
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
no code implementations • Findings (NAACL) 2022 • Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Bryan Hooi
GRAPHCACHE aggregates the features from sentences in the whole dataset to learn global representations of properties, and use them to augment the local features within individual sentences.
1 code implementation • NAACL 2022 • Yiwei Wang, Muhao Chen, Wenxuan Zhou, Yujun Cai, Yuxuan Liang, Dayiheng Liu, Baosong Yang, Juncheng Liu, Bryan Hooi
In this paper, we propose the CORE (Counterfactual Analysis based Relation Extraction) debiasing method that guides the RE models to focus on the main effects of textual context without losing the entity information.
no code implementations • 18 Dec 2021 • Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Bryan Hooi
In this work, we propose the TNS (Time-aware Neighbor Sampling) method: TNS learns from temporal information to provide an adaptive receptive neighborhood for every node at any time.
no code implementations • 8 Dec 2021 • Mingfei Chen, Jianfeng Zhang, Xiangyu Xu, Lijuan Liu, Yujun Cai, Jiashi Feng, Shuicheng Yan
Meanwhile, for achieving higher rendering efficiency, we introduce a progressive rendering pipeline through geometry guidance, which leverages the geometric feature volume and the predicted density values to progressively reduce the number of sampling points and speed up the rendering process.
no code implementations • 1 Dec 2021 • Yiwei Wang, Yujun Cai, Yuxuan Liang, Wei Wang, Henghui Ding, Muhao Chen, Jing Tang, Bryan Hooi
Representing a label distribution as a one-hot vector is a common practice in training node classification models.
no code implementations • NeurIPS 2021 • Yiwei Wang, Yujun Cai, Yuxuan Liang, Henghui Ding, Changhu Wang, Siddharth Bhatia, Bryan Hooi
To address this issue, our idea is to transform the temporal graphs using data augmentation (DA) with adaptive magnitudes, so as to effectively augment the input features and preserve the essential semantic information.
2 code implementations • NeurIPS 2021 • Tao Wang, Jianfeng Zhang, Yujun Cai, Shuicheng Yan, Jiashi Feng
Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks.
Ranked #3 on 3D Multi-Person Pose Estimation on Panoptic (using extra training data)
1 code implementation • 1 Jun 2021 • Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi
In this work, we propose the Mixup methods for two fundamental tasks in graph learning: node and graph classification.
Ranked #16 on Node Classification on Pubmed
no code implementations • ICCV 2021 • Yujun Cai, Yiwei Wang, Yiheng Zhu, Tat-Jen Cham, Jianfei Cai, Junsong Yuan, Jun Liu, Chuanxia Zheng, Sijie Yan, Henghui Ding, Xiaohui Shen, Ding Liu, Nadia Magnenat Thalmann
Notably, by considering this problem as a conditional generation process, we estimate a parametric distribution of the missing regions based on the input conditions, from which to sample and synthesize the full motion series.
no code implementations • 22 Sep 2020 • Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, Bryan Hooi
We present a new method to regularize graph neural networks (GNNs) for better generalization in graph classification.
5 code implementations • 15 Mar 2020 • Chi Zhang, Yujun Cai, Guosheng Lin, Chunhua Shen
We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance.
1 code implementation • ICCV 2019 • Yujun Cai, Liuhao Ge, Jun Liu, Jianfei Cai, Tat-Jen Cham, Junsong Yuan, Nadia Magnenat Thalmann
Despite great progress in 3D pose estimation from single-view images or videos, it remains a challenging task due to the substantial depth ambiguity and severe self-occlusions.
Ranked #146 on 3D Human Pose Estimation on Human3.6M
no code implementations • ECCV 2018 • Yujun Cai, Liuhao Ge, Jianfei Cai, Junsong Yuan
Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to substantial depth ambiguity and the difficulty of obtaining fully-annotated training data.
1 code implementation • CVPR 2018 • Liuhao Ge, Yujun Cai, Junwu Weng, Junsong Yuan
Convolutional Neural Network (CNN) has shown promising results for 3D hand pose estimation in depth images.
Ranked #7 on Hand Pose Estimation on HANDS 2017