154 citations · 491 across the 25 of their papers we have counts for
26 papers
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
Yao Zhu, Yuefeng Chen, Xiaodan Li +6
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and t…
OpenAUC: Towards AUC-Oriented Open-Set Recognition
Zitai Wang, Qianqian Xu, Zhiyong Yang +3
Traditional machine learning follows a close-set assumption that the training and test set share the same label space. While in many practical scenarios, it is inevitable that some…
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
Yangbangyan Jiang, Xiaodan Li, Yuefeng Chen +5
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL). Most state-of-the-a…
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Zitai Wang, Qianqian Xu, Zhiyong Yang +3
Top-k error has become a popular metric for large-scale classification benchmarks due to the inevitable semantic ambiguity among classes. Existing literature on top-k optimization…
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Wenzheng Hou, Qianqian Xu, Zhiyong Yang +3
It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a ty…
Optimizing Two-way Partial AUC with an End-to-end Framework
Zhiyong Yang, Qianqian Xu, Shilong Bao +3
The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive Rates (TPRs) and False Positive…
Towards Robust Vision Transformer
Xiaofeng Mao, Gege Qi, Yuefeng Chen +5
Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most…
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
Jianfeng Dong, Zhe Ma, Xiaofeng Mao +4
This paper strives to predict fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute…
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
Xiaodan Li, Jinfeng Li, Yuefeng Chen +5
We study the query-based attack against image retrieval to evaluate its robustness against adversarial examples under the black-box setting, where the adversary only has query acce…
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association
Peisong Wen, Qianqian Xu, Yangbangyan Jiang +3
Nowadays, we have witnessed the early progress on learning the association between voice and face automatically, which brings a new wave of studies to the computer vision community…
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Ranjie Duan, Xiaofeng Mao, A. K. Qin +4
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted fro…
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
Honggu Liu, Xiaodan Li, Wenbo Zhou +5
The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns. We observe that up-sampling is a necessary step o…
Hierarchical Similarity Learning for Language-based Product Image Retrieval
Zhe Ma, Fenghao Liu, Jianfeng Dong +3
This paper aims for the language-based product image retrieval task. The majority of previous works have made significant progress by designing network structure, similarity measur…
The Open Brands Dataset: Unified brand detection and recognition at scale
Xuan Jin, Wei Su, Rong Zhang +2
Intellectual property protection(IPP) have received more and more attention recently due to the development of the global e-commerce platforms. brand recognition plays a significan…
Composite Adversarial Attacks
Xiaofeng Mao, Yuefeng Chen, Shuhui Wang +3
Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness. In practice, attack algorithms are artifi…
Heuristic Domain Adaptation
Shuhao Cui, Xuan Jin, Shuhui Wang +2
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different…
Sharp Multiple Instance Learning for DeepFake Video Detection
Xiaodan Li, Yining Lang, Yuefeng Chen +5
With the rapid development of facial manipulation techniques, face forgery has received considerable attention in multimedia and computer vision community due to security concerns.…
GAP++: Learning to generate target-conditioned adversarial examples
Xiaofeng Mao, Yuefeng Chen, Yuhong Li +2
Adversarial examples are perturbed inputs which can cause a serious threat for machine learning models. Finding these perturbations is such a hard task that we can only use the ite…
Robust Generative Adversarial Network
Shufei Zhang, Zhuang Qian, Kaizhu Huang +2
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations. Most exist…
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
Zhe Ma, Jianfeng Dong, Yao Zhang +4
This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute am…
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
Xiaodan Li, Yuefeng Chen, Yuan He +1
Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible…
Learning To Characterize Adversarial Subspaces
Xiaofeng Mao, Yuefeng Chen, Yuhong Li +2
Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially de…
Self-supervised Adversarial Training
Kejiang Chen, Hang Zhou, Yuefeng Chen +6
Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in whi…
Self-Supervised Learning For Few-Shot Image Classification
Da Chen, Yuefeng Chen, Yuhong Li +3
Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast ad…