A graph structure is a powerful mathematical abstraction, which can not only represent information about individuals but also capture the interactions between individuals for …
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph …
Z Zou, W Tang - Proceedings of the IEEE/CVF international …, 2021 - openaccess.thecvf.com
The graph convolutional network (GCN) has recently achieved promising performance of 3D human pose estimation (HPE) by modeling the relationship among body parts. However …
Y Lee, JH Park, S Oh, K Shin, J Sun, M Jung… - Nature Biomedical …, 2022 - nature.com
Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here …
Scene graphs are powerful representations that parse images into their abstract semantic elements, ie, objects and their interactions, which facilitates visual comprehension and …
Y Tian, X Zhao, W Huang - Neurocomputing, 2022 - Elsevier
Compared to traditional machine learning, deep learning can learn deeper abstract data representation and understand scattered data properties. It has gained considerable …
Z Jin, X Hu, L Zhu, L Song… - Advances in Neural …, 2024 - proceedings.neurips.cc
Co-occurrent visual patterns suggest that pixel relation modeling facilitates dense prediction tasks, which inspires the development of numerous context modeling paradigms,\emph {eg} …
We present a method that achieves state-of-the-art results on challenging (few-shot) layout- to-image generation tasks by accurately modeling textures, structures and relationships …
R Zeng, W Huang, M Tan, Y Rong… - … on Pattern Analysis …, 2021 - ieeexplore.ieee.org
Temporal action localization, which requires a machine to recognize the location as well as the category of action instances in videos, has long been researched in computer vision …