no code implementations • 19 May 2022 • Qiang Li, Tao Dai, Shu-Tao Xia
Recently, deep learning methods have shown great success in 3D point cloud upsampling.
no code implementations • 3 Apr 2022 • Jiawang Bai, Li Yuan, Shu-Tao Xia, Shuicheng Yan, Zhifeng Li, Wei Liu
The transformer models have shown promising effectiveness in dealing with various vision tasks.
no code implementations • 27 Mar 2022 • Neng Wang, Yang Bai, Kun Yu, Yong Jiang, Shu-Tao Xia, Yan Wang
Face forgery has attracted increasing attention in recent applications of computer vision.
no code implementations • 22 Feb 2022 • Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Guanhao Gan, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
To explore whether adversarial training could defend against backdoor attacks or not, we conduct extensive experiments across different threat models and perturbation budgets, and find the threat model in adversarial training matters.
1 code implementation • 7 Feb 2022 • Jinpeng Wang, Bin Chen, Dongliang Liao, Ziyun Zeng, Gongfu Li, Shu-Tao Xia, Jin Xu
By performing Asymmetric-Quantized Contrastive Learning (AQ-CL) across views, HCQ aligns texts and videos at coarse-grained and multiple fine-grained levels.
1 code implementation • ICLR 2022 • Yiming Li, Haoxiang Zhong, Xingjun Ma, Yong Jiang, Shu-Tao Xia
Visual object tracking (VOT) has been widely adopted in mission-critical applications, such as autonomous driving and intelligent surveillance systems.
1 code implementation • ICML Workshop AML 2021 • Yiming Li, Linghui Zhu, Xiaojun Jia, Yong Jiang, Shu-Tao Xia, Xiaochun Cao
In this paper, we explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified \emph{external features}.
no code implementations • NeurIPS 2021 • Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang
Adversarial robustness has received increasing attention along with the study of adversarial examples.
1 code implementation • 25 Nov 2021 • Yang Bai, Xin Yan, Yong Jiang, Shu-Tao Xia, Yisen Wang
Adversarial robustness has received increasing attention along with the study of adversarial examples.
1 code implementation • 25 Nov 2021 • Sen yang, Zhicheng Wang, Ze Chen, YanJie Li, Shoukui Zhang, Zhibin Quan, Shu-Tao Xia, Yiping Bao, Erjin Zhou, Wankou Yang
This paper presents a new method to solve keypoint detection and instance association by using Transformer.
Ranked #10 on
Multi-Person Pose Estimation
on COCO test-dev
no code implementations • 29 Sep 2021 • Yinghua Gao, Dongxian Wu, Jingfeng Zhang, Shu-Tao Xia, Gang Niu, Masashi Sugiyama
Based on thorough experiments, we find that such trade-off ignores the interactions between the perturbation budget of adversarial training and the magnitude of the backdoor trigger.
no code implementations • 29 Sep 2021 • Naiqi Li, Wenjie Li, Yong Jiang, Shu-Tao Xia
In this paper we propose the deep Dirichlet process mixture (DDPM) model, which is an unsupervised method that simultaneously performs clustering and feature learning.
no code implementations • 18 Sep 2021 • Kuofeng Gao, Jiawang Bai, Bin Chen, Dongxian Wu, Shu-Tao Xia
To the best of our knowledge, this is the first attempt at the backdoor attack against deep hashing models.
1 code implementation • 11 Sep 2021 • Jinpeng Wang, Ziyun Zeng, Bin Chen, Tao Dai, Shu-Tao Xia
The high efficiency in computation and storage makes hashing (including binary hashing and quantization) a common strategy in large-scale retrieval systems.
no code implementations • 11 Sep 2021 • Ziyun Zeng, Jinpeng Wang, Bin Chen, Tao Dai, Shu-Tao Xia
Deep hashing approaches, including deep quantization and deep binary hashing, have become a common solution to large-scale image retrieval due to high computation and storage efficiency.
2 code implementations • 7 Jul 2021 • YanJie Li, Sen yang, Shoukui Zhang, Zhicheng Wang, Wankou Yang, Shu-Tao Xia, Erjin Zhou
The 2D heatmap representation has dominated human pose estimation for years due to its high performance.
no code implementations • ICML Workshop AML 2021 • Jiawang Bai, Bin Chen, Dongxian Wu, Chaoning Zhang, Shu-Tao Xia
We propose $universal \ adversarial \ head$ (UAH), which crafts adversarial query videos by prepending the original videos with a sequence of adversarial frames to perturb the normal hash codes in the Hamming space.
no code implementations • 12 Jun 2021 • Jiying Zhang, Yuzhao Chen, Xi Xiao, Runiu Lu, Shu-Tao Xia
Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data.
no code implementations • 10 Jun 2021 • Jiying Zhang, Yuzhao Chen, Xi Xiao, Runiu Lu, Shu-Tao Xia
HyperGraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph structured data.
1 code implementation • ICCV 2021 • YanJie Li, Shoukui Zhang, Zhicheng Wang, Sen yang, Wankou Yang, Shu-Tao Xia, Erjin Zhou
Most existing CNN-based methods do well in visual representation, however, lacking in the ability to explicitly learn the constraint relationships between keypoints.
no code implementations • 6 Apr 2021 • Yiming Li, Tongqing Zhai, Yong Jiang, Zhifeng Li, Shu-Tao Xia
We demonstrate that this attack paradigm is vulnerable when the trigger in testing images is not consistent with the one used for training.
1 code implementation • ICLR 2021 • Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs).
no code implementations • 6 Mar 2021 • Yiming Li, YanJie Li, Yalei Lv, Yong Jiang, Shu-Tao Xia
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data.
1 code implementation • ICLR 2021 • Jiawang Bai, Baoyuan Wu, Yong Zhang, Yiming Li, Zhifeng Li, Shu-Tao Xia
By utilizing the latest technique in integer programming, we equivalently reformulate this BIP problem as a continuous optimization problem, which can be effectively and efficiently solved using the alternating direction method of multipliers (ADMM) method.
1 code implementation • NeurIPS 2020 • Naiqi Li, Wenjie Li, Jifeng Sun, Yinghua Gao, Yong Jiang, Shu-Tao Xia
In this paper we propose Stochastic Deep Gaussian Processes over Graphs (DGPG), which are deep structure models that learn the mappings between input and output signals in graph domains.
1 code implementation • 22 Oct 2020 • Tongqing Zhai, Yiming Li, Ziqi Zhang, Baoyuan Wu, Yong Jiang, Shu-Tao Xia
We also demonstrate that existing backdoor attacks cannot be directly adopted in attacking speaker verification.
no code implementations • 18 Oct 2020 • Xingchun Xiang, Qingtao Tang, Huaixuan Zhang, Tao Dai, Jiawei Li, Shu-Tao Xia
To address this issue, we propose a novel regression tree, named James-Stein Regression Tree (JSRT) by considering global information from different nodes.
no code implementations • 16 Oct 2020 • Shudeng Wu, Tao Dai, Shu-Tao Xia
Recently, deep neural networks (DNNs) have been widely and successfully used in Object Detection, e. g.
1 code implementation • 12 Oct 2020 • Yiming Li, Ziqi Zhang, Jiawang Bai, Baoyuan Wu, Yong Jiang, Shu-Tao Xia
Based on the proposed backdoor-based watermarking, we use a hypothesis test guided method for dataset verification based on the posterior probability generated by the suspicious third-party model of the benign samples and their correspondingly watermarked samples ($i. e.$, images with trigger) on the target class.
1 code implementation • ECCV 2020 • Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting).
no code implementations • 21 Aug 2020 • Yiming Li, Jiawang Bai, Jiawei Li, Xue Yang, Yong Jiang, Shu-Tao Xia
Interpretability and effectiveness are two essential and indispensable requirements for adopting machine learning methods in reality.
no code implementations • 14 Aug 2020 • Jie Fang, Jian-Wu Lin, Shu-Tao Xia, Yong Jiang, Zhikang Xia, Xiang Liu
This paper proposes Neural Network-based Automatic Factor Construction (NNAFC), a tailored neural network framework that can automatically construct diversified financial factors based on financial domain knowledge and a variety of neural network structures.
1 code implementation • 17 Jul 2020 • Yiming Li, Yong Jiang, Zhifeng Li, Shu-Tao Xia
Backdoor attack intends to embed hidden backdoor into deep neural networks (DNNs), so that the attacked models perform well on benign samples, whereas their predictions will be maliciously changed if the hidden backdoor is activated by attacker-specified triggers.
1 code implementation • ECCV 2020 • Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
no code implementations • 1 Jul 2020 • Dongxian Wu, Yisen Wang, Zhuobin Zheng, Shu-Tao Xia
Deep neural networks (DNNs) exhibit great success on many tasks with the help of large-scale well annotated datasets.
2 code implementations • ECCV 2020 • Jiawang Bai, Bin Chen, Yiming Li, Dongxian Wu, Weiwei Guo, Shu-Tao Xia, En-hui Yang
In this paper, we propose a novel method, dubbed deep hashing targeted attack (DHTA), to study the targeted attack on such retrieval.
3 code implementations • NeurIPS 2020 • Dongxian Wu, Shu-Tao Xia, Yisen Wang
The study on improving the robustness of deep neural networks against adversarial examples grows rapidly in recent years.
no code implementations • 9 Apr 2020 • Yiming Li, Tongqing Zhai, Baoyuan Wu, Yong Jiang, Zhifeng Li, Shu-Tao Xia
Backdoor attack intends to inject hidden backdoor into the deep neural networks (DNNs), such that the prediction of the infected model will be maliciously changed if the hidden backdoor is activated by the attacker-defined trigger, while it performs well on benign samples.
no code implementations • 26 Mar 2020 • Xianbin Lv, Dongxian Wu, Shu-Tao Xia
Probabilistic modeling, which consists of a classifier and a transition matrix, depicts the transformation from true labels to noisy labels and is a promising approach.
1 code implementation • 16 Mar 2020 • Yiming Li, Baoyuan Wu, Yan Feng, Yanbo Fan, Yong Jiang, Zhifeng Li, Shu-Tao Xia
In this work, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_{stand}$ and $R_{rob}$), which is with respect to the benign example and its neighborhoods respectively.
no code implementations • 26 Feb 2020 • Yan Feng, Bin Chen, Tao Dai, Shu-Tao Xia
Deep product quantization network (DPQN) has recently received much attention in fast image retrieval tasks due to its efficiency of encoding high-dimensional visual features especially when dealing with large-scale datasets.
no code implementations • 23 Feb 2020 • Xue Yang, Yan Feng, Weijun Fang, Jun Shao, Xiaohu Tang, Shu-Tao Xia, Rongxing Lu
However, the strong defence ability and high learning accuracy of these schemes cannot be ensured at the same time, which will impede the wide application of FL in practice (especially for medical or financial institutions that require both high accuracy and strong privacy guarantee).
2 code implementations • ICLR 2020 • Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, Xingjun Ma
We find that using more gradients from the skip connections rather than the residual modules according to a decay factor, allows one to craft adversarial examples with high transferability.
no code implementations • 26 Dec 2019 • Jie Fang, Shu-Tao Xia, Jian-Wu Lin, Zhikang Xia, Xiang Liu, Yong Jiang
This paper proposes Alpha Discovery Neural Network (ADNN), a tailored neural network structure which can automatically construct diversified financial technical indicators based on prior knowledge.
no code implementations • 8 Dec 2019 • Jie Fang, Shu-Tao Xia, Jian-Wu Lin, Yong Jiang
According to neural network universal approximation theorem, pre-training can conduct a more effective and explainable evolution process.
1 code implementation • CVPR 2020 • Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, Shu-Tao Xia
In this paper, we demonstrate it can indeed help the model to output more discriminative results within old classes.
Ranked #2 on
Incremental Learning
on ImageNet100 - 10 steps
(# M Params metric)
1 code implementation • 5 Nov 2019 • Yiming Li, Peidong Liu, Yong Jiang, Shu-Tao Xia
To a large extent, the privacy of visual classification data is mainly in the mapping between the image and its corresponding label, since this relation provides a great amount of information and can be used in other scenarios.
no code implementations • 5 Nov 2019 • Peidong Liu, Xiyu Yan, Yong Jiang, Shu-Tao Xia
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network.
no code implementations • 27 Oct 2019 • Jia Xu, Yiming Li, Yong Jiang, Shu-Tao Xia
In this paper, we define the local flatness of the loss surface as the maximum value of the chosen norm of the gradient regarding to the input within a neighborhood centered on the benign sample, and discuss the relationship between the local flatness and adversarial vulnerability.
no code implementations • 25 Sep 2019 • Haoyu Liang, Zhihao Ouyang, Hang Su, Yuyuan Zeng, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang
Convolutional neural networks (CNNs) have often been treated as “black-box” and successfully used in a range of tasks.
1 code implementation • 17 Sep 2019 • Hongshan Li, Yu Guo, Zhi Wang, Shu-Tao Xia, Wenwu Zhu
Then we train the agent in a reinforcement learning way to adapt it for different deep learning cloud services that act as the {\em interactive training environment} and feeding a reward with comprehensive consideration of accuracy and data size.
Multimedia Image and Video Processing
no code implementations • 15 Aug 2019 • Qianggang Ding, Sifan Wu, Hao Sun, Jiadong Guo, Shu-Tao Xia
In addition, label regularization techniques such as label smoothing and label disturbance have also been proposed with the motivation of adding a stochastic perturbation to labels.
1 code implementation • 17 Jul 2019 • Yiming Li, Yang Zhang, Qingtao Tang, Weipeng Huang, Yong Jiang, Shu-Tao Xia
$k$-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing.
no code implementations • 13 Apr 2019 • Bowen Zhao, Xi Xiao, Wanpeng Zhang, Bin Zhang, Shu-Tao Xia
There is a probabilistic version of PCA, known as Probabilistic PCA (PPCA).
no code implementations • 14 Mar 2019 • Jiawang Bai, Yiming Li, Jiawei Li, Yong Jiang, Shu-Tao Xia
How to obtain a model with good interpretability and performance has always been an important research topic.
no code implementations • 10 Mar 2019 • Yiming Li, Jiawang Bai, Jiawei Li, Xue Yang, Yong Jiang, Chun Li, Shu-Tao Xia
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood.
no code implementations • NeurIPS 2018 • Songtao Wang, Dan Li, Yang Cheng, Jinkun Geng, Yanshu Wang, Shuai Wang, Shu-Tao Xia, Jianping Wu
In distributed machine learning (DML), the network performance between machines significantly impacts the speed of iterative training.
no code implementations • WS 2018 • Jilei Wang, Shiying Luo, Weiyan Shi, Tao Dai, Shu-Tao Xia
Learning vector space representation of words (i. e., word embeddings) has recently attracted wide research interests, and has been extended to cross-lingual scenario.
2 code implementations • ICML 2018 • Xingjun Ma, Yisen Wang, Michael E. Houle, Shuo Zhou, Sarah M. Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, James Bailey
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
Ranked #33 on
Image Classification
on mini WebVision 1.0
1 code implementation • CVPR 2018 • Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, Shu-Tao Xia
We refer to this more complex scenario as the \textbf{open-set noisy label} problem and show that it is nontrivial in order to make accurate predictions.
no code implementations • 21 Apr 2016 • Chaobing Song, Shu-Tao Xia
In this paper, we propose a new discriminative model named \emph{nonextensive information theoretical machine (NITM)} based on nonextensive generalization of Shannon information theory.
no code implementations • 15 Apr 2016 • Chaobing Song, Shu-Tao Xia
In this paper, we propose a Bayesian linear regression model with Student-t assumptions (BLRS), which can be inferred exactly.
no code implementations • 25 Nov 2015 • Yisen Wang, Chaobing Song, Shu-Tao Xia
In this paper, a Tsallis Entropy Criterion (TEC) algorithm is proposed to unify Shannon entropy, Gain Ratio and Gini index, which generalizes the split criteria of decision trees.