[Retracted] U‐Net‐Based Medical Image Segmentation

XX Yin, L Sun, Y Fu, R Lu… - Journal of healthcare …, 2022 - Wiley Online Library
Deep learning has been extensively applied to segmentation in medical imaging. U‐Net
proposed in 2015 shows the advantages of accurate segmentation of small targets and its …

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

H Phan, K Mikkelsen - Physiological Measurement, 2022 - iopscience.iop.org
Modern deep learning holds a great potential to transform clinical studies of human sleep.
Teaching a machine to carry out routine tasks would be a tremendous reduction in workload …

Lgm: Large multi-view gaussian model for high-resolution 3d content creation

J Tang, Z Chen, X Chen, T Wang, G Zeng… - European Conference on …, 2025 - Springer
Abstract 3D content creation has achieved significant progress in terms of both quality and
speed. Although current feed-forward models can produce 3D objects in seconds, their …

Anydoor: Zero-shot object-level image customization

X Chen, L Huang, Y Liu, Y Shen… - Proceedings of the …, 2024 - openaccess.thecvf.com
This work presents AnyDoor a diffusion-based image generator with the power to teleport
target objects to new scenes at user-specified locations with desired shapes. Instead of …

UIU-Net: U-Net in U-Net for infrared small object detection

X Wu, D Hong, J Chanussot - IEEE Transactions on Image …, 2022 - ieeexplore.ieee.org
Learning-based infrared small object detection methods currently rely heavily on the
classification backbone network. This tends to result in tiny object loss and feature …

Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation

T Wu, J Zhang, X Fu, Y Wang, J Ren… - Proceedings of the …, 2023 - openaccess.thecvf.com
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of
large-scale real-scanned 3D databases. To facilitate the development of 3D perception …

Texture: Text-guided texturing of 3d shapes

E Richardson, G Metzer, Y Alaluf, R Giryes… - ACM SIGGRAPH 2023 …, 2023 - dl.acm.org
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and
transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion model …

Mvimgnet: A large-scale dataset of multi-view images

X Yu, M Xu, Y Zhang, H Liu, C Ye… - Proceedings of the …, 2023 - openaccess.thecvf.com
Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

Sketch-guided text-to-image diffusion models

A Voynov, K Aberman, D Cohen-Or - ACM SIGGRAPH 2023 Conference …, 2023 - dl.acm.org
Text-to-Image models have introduced a remarkable leap in the evolution of machine
learning, demonstrating high-quality synthesis of images from a given text-prompt. However …

Kubric: A scalable dataset generator

K Greff, F Belletti, L Beyer, C Doersch… - Proceedings of the …, 2022 - openaccess.thecvf.com
Data is the driving force of machine learning, with the amount and quality of training data
often being more important for the performance of a system than architecture and training …