2 citations · 4 across the 6 of their papers we have counts for
7 papers
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, Bin Xiao, Chao Wang +86
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…
HIRL: A General Framework for Hierarchical Image Representation Learning
Minghao Xu, Yuanfan Guo, Xuanyu Zhu +5
Learning self-supervised image representations has been broadly studied to boost various visual understanding tasks. Existing methods typically learn a single level of image semant…
BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources
Xuanyu Zhu, Yang Gao, Feng Liu +2
Introduction: Background field removal (BFR) is a critical step required for successful quantitative susceptibility mapping (QSM). However, eliminating the background field in brai…
HCSC: Hierarchical Contrastive Selective Coding
Yuanfan Guo, Minghao Xu, Jiawen Li +4
Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coa…
Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning
Xuanyu Zhu, Yang Gao, Feng Liu +2
Introduction: Quantitative Susceptibility Mapping (QSM) is generally acquired with full brain coverage, even though many QSM brain-iron studies focus on the deep grey matter (DGM)…
xQSM: Quantitative Susceptibility Mapping with Octave Convolutional and Noise Regularized Neural Networks
Yang Gao, Xuanyu Zhu, Bradford A. Moffat +6
Quantitative susceptibility mapping (QSM) is a valuable magnetic resonance imaging (MRI) contrast mechanism that has demonstrated broad clinical applications. However, the image re…