1 code implementation • 17 Nov 2022 • Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, Michael Backes
In this work, we design good practices to address these limitations, and we present the first comprehensive evaluation of transfer attacks, covering 23 representative attacks against 9 defenses on ImageNet.
1 code implementation • 18 Oct 2023 • Zhengyu Zhao, Hanwei Zhang, Renjue Li, Ronan Sicre, Laurent Amsaleg, Michael Backes, Qi Li, Chao Shen
Transferable adversarial examples raise critical security concerns in real-world, black-box attack scenarios.
1 code implementation • 4 Dec 2019 • Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg
Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input.
1 code implementation • 28 Mar 2019 • Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg
This paper investigates the visual quality of the adversarial examples.
no code implementations • 5 Jun 2021 • Renjue Li, Hanwei Zhang, Pengfei Yang, Cheng-Chao Huang, Aimin Zhou, Bai Xue, Lijun Zhang
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
no code implementations • 18 Aug 2022 • Fangquan Lin, Wei Jiang, Hanwei Zhang, Cheng Yang
KDD CUP 2022 proposes a time-series forecasting task on spatial dynamic wind power dataset, in which the participants are required to predict the future generation given the historical context factors.
no code implementations • 5 Oct 2022 • Hanwei Zhang, Hideaki Uchiyama, Shintaro Ono, Hiroshi Kawasaki
In this paper, we present MOTSLAM, a dynamic visual SLAM system with the monocular configuration that tracks both poses and bounding boxes of dynamic objects.
no code implementations • 17 Jan 2023 • Hanwei Zhang, Felipe Torres, Ronan Sicre, Yannis Avrithis, Stephane Ayache
Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps.
no code implementations • 15 Sep 2023 • Wei Jiang, Zhongkai Yi, Li Wang, Hanwei Zhang, Jihai Zhang, Fangquan Lin, Cheng Yang
Aggregating distributed energy resources in power systems significantly increases uncertainties, in particular caused by the fluctuation of renewable energy generation.
no code implementations • 13 Feb 2024 • Zi Ye, Tianxiang Chen, Fangyijie Wang, Hanwei Zhang, Guanxi Li, Lijun Zhang
In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is important since it can determine whether urgent intervention is required in many emergencies.
no code implementations • 23 Apr 2024 • Ronan Sicre, Hanwei Zhang, Julien Dejasmin, Chiheb Daaloul, Stéphane Ayache, Thierry Artières
This paper presents Discriminative Part Network (DP-Net), a deep architecture with strong interpretation capabilities, which exploits a pretrained Convolutional Neural Network (CNN) combined with a part-based recognition module.
no code implementations • 23 Apr 2024 • Felipe Torres Figueroa, Hanwei Zhang, Ronan Sicre, Yannis Avrithis, Stephane Ayache
This paper studies interpretability of convolutional networks by means of saliency maps.
no code implementations • 23 Apr 2024 • Felipe Torres, Hanwei Zhang, Ronan Sicre, Stéphane Ayache, Yannis Avrithis
Explanations obtained from transformer-based architectures in the form of raw attention, can be seen as a class-agnostic saliency map.
no code implementations • 24 Apr 2024 • Hanwei Zhang, Ying Zhu, Dan Wang, Lijun Zhang, Tianxiang Chen, Zi Ye
This encompasses general visual tasks, Medical visual tasks (e. g., 2D / 3D segmentation, classification, and image registration, etc.