96 citations · 580 across the 59 of their papers we have counts for
60 papers
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
Qingrui Cai, Liuhong Zhu, Jianjun Zhou +3
Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic…
CoNe: Contrast Your Neighbours for Supervised Image Classification
Mingkai Zheng, Shan You, Lang Huang +5
Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping…
SimMatchV2: Semi-Supervised Learning with Graph Consistency
Mingkai Zheng, Shan You, Lang Huang +4
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a…
Can Large Language Models Empower Molecular Property Prediction?
Chen Qian, Huayi Tang, Zhirui Yang +2
Molecular property prediction has gained significant attention due to its transformative potential in multiple scientific disciplines. Conventionally, a molecule graph can be repre…
DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering
Wei Cheng, Ruixiang Chen, Wanqi Yin +18
Realistic human-centric rendering plays a key role in both computer vision and computer graphics. Rapid progress has been made in the algorithm aspect over the years, yet existing…
RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
Dongwei Pan, Long Zhuo, Jingtan Piao +13
Synthesizing high-fidelity head avatars is a central problem for computer vision and graphics. While head avatar synthesis algorithms have advanced rapidly, the best ones still fac…
Knowledge Diffusion for Distillation
Tao Huang, Yuan Zhang, Mingkai Zheng +4
The representation gap between teacher and student is an emerging topic in knowledge distillation (KD). To reduce the gap and improve the performance, current methods often resort…
Can GPT-4 Perform Neural Architecture Search?
Mingkai Zheng, Xiu Su, Shan You +4
We investigate the potential of GPT-4~\cite{gpt4} to perform Neural Architecture Search (NAS) -- the task of designing effective neural architectures. Our proposed approach, \textb…
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling
Zhitao Yang, Zhongang Cai, Haiyi Mei +12
Synthetic data has emerged as a promising source for 3D human research as it offers low-cost access to large-scale human datasets. To advance the diversity and annotation quality o…
OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation
Tong Wu, Jiarui Zhang, Xiao Fu +9
Recent advances in modeling 3D objects mostly rely on synthetic datasets due to the lack of large-scale realscanned 3D databases. To facilitate the development of 3D perception, re…
Information scrambling and entanglement in quantum approximate optimization algorithm circuits
Chen Qian, Wei-Feng Zhuang, Rui-Cheng Guo +2
Variational quantum algorithms, which consist of optimal parameterized quantum circuits, are promising for demonstrating quantum advantages in the noisy intermediate-scale quantum…
Influence-Based Mini-Batching for Graph Neural Networks
Johannes Gasteiger, Chendi Qian, Stephan Günnemann
Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches. To solve this, previous methods have relied on sampling or gra…
A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization
Jian Cao, Chen Qian, Yihui Huang +5
Implicit regularization is an important way to interpret neural networks. Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF)…
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Yirong Zhou, Chen Qian, Jiayu Li +11
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (M…
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Junjie Chen, Li Niu, Siyuan Zhou +3
Semantic segmentation is an important and prevalent task, but severely suffers from the high cost of pixel-level annotations when extending to more classes in wider applications. T…
PoseTrans: A Simple Yet Effective Pose Transformation Augmentation for Human Pose Estimation
Wentao Jiang, Sheng Jin, Wentao Liu +3
Human pose estimation aims to accurately estimate a wide variety of human poses. However, existing datasets often follow a long-tailed distribution that unusual poses only occupy a…
3D Interacting Hand Pose Estimation by Hand De-occlusion and Removal
Hao Meng, Sheng Jin, Wentao Liu +4
Estimating 3D interacting hand pose from a single RGB image is essential for understanding human actions. Unlike most previous works that directly predict the 3D poses of two inter…
Pose for Everything: Towards Category-Agnostic Pose Estimation
Lumin Xu, Sheng Jin, Wang Zeng +5
Existing works on 2D pose estimation mainly focus on a certain category, e.g. human, animal, and vehicle. However, there are lots of application scenarios that require detecting th…
Structure-aware Editable Morphable Model for 3D Facial Detail Animation and Manipulation
Jingwang Ling, Zhibo Wang, Ming Lu +3
Morphable models are essential for the statistical modeling of 3D faces. Previous works on morphable models mostly focus on large-scale facial geometry but ignore facial details. T…
ScaleNet: Searching for the Model to Scale
Jiyang Xie, Xiu Su, Shan You +3
Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales. Current methods either simply resort…
LightViT: Towards Light-Weight Convolution-Free Vision Transformers
Tao Huang, Lang Huang, Shan You +3
Vision transformers (ViTs) are usually considered to be less light-weight than convolutional neural networks (CNNs) due to the lack of inductive bias. Recent works thus resort to c…
HEAD: HEtero-Assists Distillation for Heterogeneous Object Detectors
Luting Wang, Xiaojie Li, Yue Liao +5
Conventional knowledge distillation (KD) methods for object detection mainly concentrate on homogeneous teacher-student detectors. However, the design of a lightweight detector for…
Submission to Generic Event Boundary Detection Challenge@CVPR 2022: Local Context Modeling and Global Boundary Decoding Approach
Jiaqi Tang, Zhaoyang Liu, Jing Tan +3
Generic event boundary detection (GEBD) is an important yet challenging task in video understanding, which aims at detecting the moments where humans naturally perceive event bound…
Ordered Subgraph Aggregation Networks
Chendi Qian, Gaurav Rattan, Floris Geerts +2
Numerous subgraph-enhanced graph neural networks (GNNs) have emerged recently, provably boosting the expressive power of standard (message-passing) GNNs. However, there is a limite…