no code implementations • 4 Mar 2024 • Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu
Despite the success of the carefully-annotated benchmarks, the effectiveness of existing graph neural networks (GNNs) can be considerably impaired in practice when the real-world graph data is noisily labeled.
no code implementations • 12 Dec 2023 • Renyang Liu, Wei Zhou, Xin Jin, Song Gao, Yuanyu Wang, Ruxin Wang
In generating adversarial examples, the conventional black-box attack methods rely on sufficient feedback from the to-be-attacked models by repeatedly querying until the attack is successful, which usually results in thousands of trials during an attack.
no code implementations • 22 Nov 2023 • Xiao Song, Jiafan Liu, Yun Li, Wenbin Lei, Ruxin Wang
Radiology Report Generation (RRG) draws attention as an interaction between vision and language fields.
no code implementations • 4 Jun 2023 • Shuo Ye, Yufeng Shi, Ruxin Wang, Yu Wang, Jiamiao Xu, Chuanwu Yang, Xinge You
Data is the foundation for the development of computer vision, and the establishment of datasets plays an important role in advancing the techniques of fine-grained visual categorization~(FGVC).
no code implementations • 1 Jan 2021 • Bingbing Song, wei he, Renyang Liu, Shui Yu, Ruxin Wang, Mingming Gong, Tongliang Liu, Wei Zhou
Several state-of-the-arts start from improving the inter-class separability of training samples by modifying loss functions, where we argue that the adversarial samples are ignored and thus limited robustness to adversarial attacks is resulted.
no code implementations • 26 Oct 2020 • Guosheng Cui, Ruxin Wang, Dan Wu, Ye Li
In recent years, semi-supervised multi-view nonnegative matrix factorization (MVNMF) algorithms have achieved promising performances for multi-view clustering.
no code implementations • 12 Sep 2020 • Chaojie Ji, Hongwei Chen, Ruxin Wang, Yunpeng Cai, Hongyan Wu
Clustering the nodes of an attributed graph, in which each node is associated with a set of feature attributes, has attracted significant attention.
no code implementations • 14 Aug 2020 • Chaojie Ji, Yijia Zheng, Ruxin Wang, Yunpeng Cai, Hongyan Wu
In this study, we present a novel molecular optimization paradigm, Graph Polish, which changes molecular optimization from the traditional "two-language translating" task into a "single-language polishing" task.
1 code implementation • 3 May 2020 • Ruxin Wang, Shuyuan Chen, Chaojie Ji, Jianping Fan, Ye Li
In this paper, we formulate a boundary-aware context neural network (BA-Net) for 2D medical image segmentation to capture richer context and preserve fine spatial information.
no code implementations • 21 Apr 2020 • Chaojie Ji, Ruxin Wang, Hongyan Wu
While graph neural networks (GNNs) have shown a great potential in various tasks on graph, the lack of transparency has hindered understanding how GNNs arrived at its predictions.
no code implementations • 17 Apr 2020 • Ruxin Wang, Shuyuan Chen, Chaojie Ji, Ye Li
In this paper, we formulate a cascaded context enhancement neural network for automatic skin lesion segmentation.
no code implementations • 9 Apr 2020 • Chaojie Ji, Ruxin Wang, Rongxiang Zhu, Yunpeng Cai, Hongyan Wu
Due to the cost of labeling nodes, classifying a node in a sparsely labeled graph while maintaining the prediction accuracy deserves attention.
no code implementations • 28 Aug 2017 • Ruxin Wang, Wei Lu, Shijun Xiang, Xianfeng Zhao, Jinwei Wang
In this paper, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain.
no code implementations • 24 Sep 2014 • Ruxin Wang, DaCheng Tao
This paper comprehensively reviews the recent development of image deblurring, including non-blind/blind, spatially invariant/variant deblurring techniques.
no code implementations • 18 Sep 2014 • Ruxin Wang, Congying Han, Yanping Wu, Tiande Guo
Fingerprint classification is an effective technique for reducing the candidate numbers of fingerprints in the stage of matching in automatic fingerprint identification system (AFIS).