Search Results for author: Jing Zhu

Found 17 papers, 4 papers with code

BClean: A Bayesian Data Cleaning System

1 code implementation11 Nov 2023 Jianbin Qin, Sifan Huang, Yaoshu Wang, Jing Zhu, Yifan Zhang, Yukai Miao, Rui Mao, Makoto Onizuka, Chuan Xiao

By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.

Bayesian Inference graph partitioning

Spatial Attention-based Distribution Integration Network for Human Pose Estimation

no code implementations9 Nov 2023 Sihan Gao, Jing Zhu, Xiaoxuan Zhuang, Zhaoyue Wang, Qijin Li

The RFM incorporates a dilated residual block and attention mechanism to expand receptive fields while enhancing sensitivity to spatial information.

Pose Estimation

TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning

no code implementations25 Sep 2023 Jing Zhu, Xiang Song, Vassilis N. Ioannidis, Danai Koutra, Christos Faloutsos

How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?

Domain Adaptation Graph Learning +2

SpotTarget: Rethinking the Effect of Target Edges for Link Prediction in Graph Neural Networks

no code implementations1 Jun 2023 Jing Zhu, YuHang Zhou, Vassilis N. Ioannidis, Shengyi Qian, Wei Ai, Xiang Song, Danai Koutra

Particularly, in prevalent GNN frameworks (e. g., DGL and PyTorch-Geometric), the target edges (i. e., the edges being predicted) consistently exist as message passing edges in the graph during training.

Link Prediction Node Classification +1

Touch and Go: Learning from Human-Collected Vision and Touch

no code implementations22 Nov 2022 Fengyu Yang, Chenyang Ma, Jiacheng Zhang, Jing Zhu, Wenzhen Yuan, Andrew Owens

The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.

Image Stylization

Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition

no code implementations13 Sep 2022 Huayu Chen, Huanhuan He, Jing Zhu, Shuting Sun, Jianxiu Li, Xuexiao Shao, Junxiang Li, Xiaowei Li, Bin Hu

Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results.

EEG Electroencephalogram (EEG) +1

CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment

1 code implementation23 Aug 2022 Jing Zhu, Danai Koutra, Mark Heimann

Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains.

Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection

no code implementations24 Jan 2022 Michael Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul, Jing Zhu, Hui Wen Loh, Prabal Datta Barua, U. Rajendra Arharya

In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.

Lung Nodule Detection

ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation

no code implementations7 Aug 2021 HaoYu Fang, Jing Zhu, Yi Fang

Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects.

Autonomous Driving

Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding

1 code implementation26 Feb 2021 Jing Zhu, Xingyu Lu, Mark Heimann, Danai Koutra

While most network embedding techniques model the relative positions of nodes in a network, recently there has been significant interest in structural embeddings that model node role equivalences, irrespective of their distances to any specific nodes.

Graph Embedding Network Embedding

Modeling Long-Term and Short-Term Interests with Parallel Attentions for Session-based Recommendation

no code implementations27 Jun 2020 Jing Zhu, Yanan Xu, Yanmin Zhu

First, most of the attention-based methods only simply utilize the last clicked item to represent the user's short-term interest ignoring the temporal information and behavior context, which may fail to capture the recent preference of users comprehensively.

Session-Based Recommendations

Structure-Attentioned Memory Network for Monocular Depth Estimation

no code implementations10 Sep 2019 Jing Zhu, Yunxiao Shi, Mengwei Ren, Yi Fang, Kuo-Chin Lien, Junli Gu

To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain.

Domain Adaptation Monocular Depth Estimation

Learning Object-specific Distance from a Monocular Image

no code implementations ICCV 2019 Jing Zhu, Yi Fang, Husam Abu-Haimed, Kuo-Chin Lien, Dongdong Fu, Junli Gu

Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving.

Autonomous Driving object-detection +1

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