Author details
Author's Published Works
Author details
Showing 1-6 of 6 results
- Sort
Filter Results
Show
Sort By
Results
Video Motion Blur Attack via Grad-Weighted and Discrete-Fusion based Perturbation Generation
IEEE Transactions on Multimedia
Year: 2025 | Early Access Article |
Recent research has shown that deep learning networks are vulnerable to adversarial samples. Although there has been great progress in the study of adversarial attacks on images, there is relatively little research on adversarial attacks in the video domain, especially on intrinsic factors of videos, such as motion blur. In this paper, we devise a novel Grad-Weighted based One-step Motion Blur Att...Show More
Focus Your Attention: Multiple Instance Learning with Attention Modification for Whole Slide Pathological Image Classification
Hailun Cheng;Shenjin Huang;Linghan Cai;Yangfan Xu;Runming Wang;Yongbing Zhang
IEEE Transactions on Circuits and Systems for Video Technology
Year: 2025 | Early Access Article |
Computer-aided pathology diagnosis based on whole slide images, which is often formulated as a weakly supervised multiple instance learning (MIL) paradigm. Current approaches generally employ attention mechanisms to aggregate instance-level features. However, the weakly supervised signal and the imbalanced instance distribution often lead to inaccurate attention localization, compromising the perf...Show More
Research on Art Design Algorithm of ADKGAN Antique Bowl Bottom
2024 IEEE 2nd International Conference on Image Processing and Computer Applications (ICIPCA)
Year: 2024 | Conference Paper |
This article proposes an ADKGAN (Adaptive bottom layer adversarial generation network) algorithm for simulating the integration of the bottom art information and modern design in a large number of traditional ancient ceramics. Because of inheriting traditional elements, this algorithm provides many modern applications design solutions that are different from traditional ones for the diversity and ...Show More
PPG-based Continuous BP Waveform Estimation Using Polarized Attention-guided Conditional Adversarial Learning Model
Chenbin Ma;Yangfan Xu;Peng Zhang;Fan Song;Yangyang Sun;Youdan Feng;Yufang He;Guanglei Zhang
IEEE Journal of Biomedical and Health Informatics
Year: 2023 | Early Access Article |
Cited by: Papers (6)
The blood pressure (BP) waveform is a vital source of physiological and pathological information concerning the cardiovascular system. This study proposes a novel attention-guided conditional generative adversarial network (cGAN), named PPG2BP-cGAN, to estimate BP waveforms based on photoplethysmography (PPG) signals. The proposed model comprises a generator and a discriminator. Specifically, the ...Show More
Imperceptible Adversarial Attack With Multigranular Spatiotemporal Attention for Video Action Recognition
IEEE Internet of Things Journal
Cited by: Papers (4)
In recent years, the application of video Internet of Things (IoT) in various cities and public places has brought unprecedented opportunities to the security field and achieved great success. However, the latest research shows that video recognition models are also vulnerable to adversarial examples, but adversarial examples based on physical attacks are easily detected by humans, making it diffi...Show More
TMN: Temporal-guided Multiattention Network for Action Recognition
Yongkang Zhang;Han Zhang;Guoming Wu;Yangfan Xu;Zhiping Shi;Jun Li
2022 26th International Conference on Pattern Recognition (ICPR)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
2D convolutional neural network, due to its low computational complexity and fast recognition speed, has attracted more and more attention from researchers in the field of video action recognition. Temporal shift and temporal differential, have made tremendous progress, but the lack of crucial spatiotemporal attention mechanism has led to huge performance loss. To address this issue, we propose a ...Show More
IEEE Account
Purchase Details
Profile Information
Need Help?
- US & Canada: +1 800 678 4333
- Worldwide: +1 732 981 0060
- Contact & Support
- About IEEE Xplore
- Contact Us
- Help
- Accessibility
- Terms of Use
- Nondiscrimination Policy
- Sitemap
- Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.
© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.