R Wang, T Lei, R Cui, B Zhang, H Meng… - IET image …, 2022 - Wiley Online Library
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A …
CY Wang, A Bochkovskiy… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Real-time object detection is one of the most important research topics in computer vision. As new approaches regarding architecture optimization and training optimization are …
Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth …
In this paper, we propose a conceptually simple but very effective attention module for Convolutional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial …
Abstract Despite that Convolutional Neural Networks (CNNs) have achieved promising performance in many medical image segmentation tasks, they rely on a large set of labeled …
Q Shi, M Liu, S Li, X Liu, F Wang… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Change detection (CD) aims to identify surface changes from bitemporal images. In recent years, deep learning (DL)-based methods have made substantial breakthroughs in the field …
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to …
Recently, pure transformer-based models have shown great potentials for vision tasks such as image classification and detection. However, the design of transformer networks is …
In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across …