Contrast-Agnostic Groupwise Registration by Robust PCA for Quantitative Cardiac MRI

no code implementations • 3 Nov 2023 • Xinqi Li, Yi Zhang, Yidong Zhao, Jan van Gemert, Qian Tao

To address the challenge, we propose a novel motion correction framework based on robust principle component analysis (rPCA) that decomposes quantitative cardiac MRI into low-rank and sparse components, and we integrate the groupwise CNN-based registration backbone within the rPCA framework.

OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

1 code implementation • 28 Oct 2021 • Jinhui Yuan, Xinqi Li, Cheng Cheng, Juncheng Liu, Ran Guo, Shenghang Cai, Chi Yao, Fei Yang, Xiaodong Yi, Chuan Wu, Haoran Zhang, Jie Zhao

Aiming at a simple, neat redesign of distributed deep learning frameworks for various parallelism paradigms, we present OneFlow, a novel distributed training framework based on an SBP (split, broadcast and partial-value) abstraction and the actor model.

Zero-Shot Fine-Grained Classification by Deep Feature Learning with Semantics

no code implementations • 4 Jul 2017 • Aoxue Li, Zhiwu Lu, Li-Wei Wang, Tao Xiang, Xinqi Li, Ji-Rong Wen

In this paper, to address the two issues, we propose a two-phase framework for recognizing images from unseen fine-grained classes, i. e. zero-shot fine-grained classification.

Classification Domain Adaptation +3