AI Medical Compendium Journal:
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

Showing 21 to 30 of 191 articles

Toward Understanding and Boosting Adversarial Transferability From a Distribution Perspective.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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 then attack the unknown target model successfully, which brings a sever...

Learning Transferable Parameters for Unsupervised Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Unsupervised domain adaptation (UDA) enables a learning machine to adapt from a labeled source domain to an unlabeled target domain under the distribution shift. Thanks to the strong representation ability of deep neural networks, recent remarkable a...

Crosslink-Net: Double-Branch Encoder Network via Fusing Vertical and Horizontal Convolutions for Medical Image Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges caused by various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architectures...

Frequency-Tuned Universal Adversarial Attacks on Texture Recognition.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Although deep neural networks (DNNs) have been shown to be susceptible to image-agnostic adversarial attacks on natural image classification problems, the effects of such attacks on DNN-based texture recognition have yet to be explored. As part of ou...

A Dual-Branch Self-Boosting Framework for Self-Supervised 3D Hand Pose Estimation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Although 3D hand pose estimation has made significant progress in recent years with the development of the deep neural network, most learning-based methods require a large amount of labeled data that is time-consuming to collect. In this paper, we pr...

MetaAge: Meta-Learning Personalized Age Estimators.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Different people age in different ways. Learning a personalized age estimator for each person is a promising direction for age estimation given that it better models the personalization of aging processes. However, most existing personalized methods ...

Dynamic Instance Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Most existing studies on unsupervised domain adaptation (UDA) assume that each domain's training samples come with domain labels (e.g., painting, photo). Samples from each domain are assumed to follow the same distribution and the domain labels are e...

A Multistage Framework With Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem and propose a novel multi-stage framework to solve real-world situations when the target data are unlabeled and arriving online sequentially in batches. Most of the tr...

Grammar-Induced Wavelet Network for Human Parsing.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Most existing methods of human parsing still face a challenge: how to extract the accurate foreground from similar or cluttered scenes effectively. In this paper, we propose a Grammar-induced Wavelet Network (GWNet), to deal with the challenge. GWNet...

MetaLabelNet: Learning to Generate Soft-Labels From Noisy-Labels.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Real-world datasets commonly have noisy labels, which negatively affects the performance of deep neural networks (DNNs). In order to address this problem, we propose a label noise robust learning algorithm, in which the base classifier is trained on ...