27 citations · 58 across the 28 of their papers we have counts for
28 papers
Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging
Xinyu Peng, Ziyang Zheng, Wenrui Dai +5
Task-adapted compressed sensing magnetic resonance imaging (CS-MRI) is emerging to address the specific demands of downstream clinical tasks with significantly fewer k-space measur…
Towards Holistic Modeling for Video Frame Interpolation with Auto-regressive Diffusion Transformers
Xinyu Peng, Han Li, Yuyang Huang +7
Existing video frame interpolation (VFI) methods often adopt a frame-centric approach, processing videos as independent short segments (e.g., triplets), which leads to temporal inc…
Error-Propagation-Free Learned Video Compression With Dual-Domain Progressive Temporal Alignment
Han Li, Shaohui Li, Wenrui Dai +5
Existing frameworks for learned video compression suffer from a dilemma between inaccurate temporal alignment and error propagation for motion estimation and compensation (ME/MC).…
3DGabSplat: 3D Gabor Splatting for Frequency-adaptive Radiance Field Rendering
Junyu Zhou, Yuyang Huang, Wenrui Dai +5
Recent prominence in 3D Gaussian Splatting (3DGS) has enabled real-time rendering while maintaining high-fidelity novel view synthesis. However, 3DGS resorts to the Gaussian functi…
METEOR: Multi-Encoder Collaborative Token Pruning for Efficient Vision Language Models
Yuchen Liu, Yaoming Wang, Bowen Shi +5
Vision encoders serve as the cornerstone of multimodal understanding. Single-encoder architectures like CLIP exhibit inherent constraints in generalizing across diverse multimodal…
Noise Conditional Variational Score Distillation
Xinyu Peng, Ziyang Zheng, Yaoming Wang +6
We propose Noise Conditional Variational Score Distillation (NCVSD), a novel method for distilling pretrained diffusion models into generative denoisers. We achieve this by reveali…
HiPART: Hierarchical Pose AutoRegressive Transformer for Occluded 3D Human Pose Estimation
Hongwei Zheng, Han Li, Wenrui Dai +4
Existing 2D-to-3D human pose estimation (HPE) methods struggle with the occlusion issue by enriching information like temporal and visual cues in the lifting stage. In this paper,…
On Disentangled Training for Nonlinear Transform in Learned Image Compression
Han Li, Shaohui Li, Wenrui Dai +5
Learned image compression (LIC) has demonstrated superior rate-distortion (R-D) performance compared to traditional codecs, but is challenged by training inefficiency that could in…
Point Cloud Denoising With Fine-Granularity Dynamic Graph Convolutional Networks
Wenqiang Xu, Wenrui Dai, Duoduo Xue +4
Due to limitations in acquisition equipment, noise perturbations often corrupt 3-D point clouds, hindering down-stream tasks such as surface reconstruction, rendering, and further…
Point Cloud Resampling with Learnable Heat Diffusion
Wenqiang Xu, Wenrui Dai, Duoduo Xue +4
Generative diffusion models have shown empirical successes in point cloud resampling, generating a denser and more uniform distribution of points from sparse or noisy 3D point clou…
Image Compression for Machine and Human Vision with Spatial-Frequency Adaptation
Han Li, Shaohui Li, Shuangrui Ding +5
Image compression for machine and human vision (ICMH) has gained increasing attention in recent years. Existing ICMH methods are limited by high training and storage overheads due…
Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance
Xinyu Peng, Ziyang Zheng, Wenrui Dai +4
Recent diffusion models provide a promising zero-shot solution to noisy linear inverse problems without retraining for specific inverse problems. In this paper, we reveal that rece…
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
Rui Yang, Wenrui Dai, Chenglin Li +3
Single-cell RNA sequencing (scRNA-seq) technology provides high-throughput gene expression data to study the cellular heterogeneity and dynamics of complex organisms. Graph neural…
AiluRus: A Scalable ViT Framework for Dense Prediction
Jin Li, Yaoming Wang, Xiaopeng Zhang +6
Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, when it comes to handling long token sequences,…
Frequency-Aware Transformer for Learned Image Compression
Han Li, Shaohui Li, Wenrui Dai +3
Learned image compression (LIC) has gained traction as an effective solution for image storage and transmission in recent years. However, existing LIC methods are redundant in late…
Learned Lossless Compression for JPEG via Frequency-Domain Prediction
Jixiang Luo, Shaohui Li, Wenrui Dai +3
JPEG images can be further compressed to enhance the storage and transmission of large-scale image datasets. Existing learned lossless compressors for RGB images cannot be well tra…
Optimization-based Block Coordinate Gradient Coding for Mitigating Partial Stragglers in Distributed Learning
Qi Wang, Ying Cui, Chenglin Li +2
Gradient coding schemes effectively mitigate full stragglers in distributed learning by introducing identical redundancy in coded local partial derivatives corresponding to all mod…
Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling
Mingxing Xu, Chenglin Li, Wenrui Dai +4
Pooling and unpooling are two essential operations in constructing hierarchical spherical convolutional neural networks (HS-CNNs) for comprehensive feature learning in the spherica…
Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks
Ziyang Zheng, Wenrui Dai, Duoduo Xue +3
It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with lea…
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning
Mingxing Xu, Wenrui Dai, Chenglin Li +2
Graph pooling has been increasingly considered for graph neural networks (GNNs) to facilitate hierarchical graph representation learning. Existing graph pooling methods commonly co…
Optimization-based Block Coordinate Gradient Coding
Qi Wang, Ying Cui, Chenglin Li +2
Existing gradient coding schemes introduce identical redundancy across the coordinates of gradients and hence cannot fully utilize the computation results from partial stragglers.…
Graph Neural Networks With Lifting-based Adaptive Graph Wavelets
Mingxing Xu, Wenrui Dai, Chenglin Li +3
Spectral-based graph neural networks (SGNNs) have been attracting increasing attention in graph representation learning. However, existing SGNNs are limited in implementing graph f…
Message Passing in Graph Convolution Networks via Adaptive Filter Banks
Xing Gao, Wenrui Dai, Chenglin Li +3
Graph convolution networks, like message passing graph convolution networks (MPGCNs), have been a powerful tool in representation learning of networked data. However, when data is…
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification
Rui Yang, Wenrui Dai, Chenglin Li +2
Message passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing methods for message passing simply sum or average all the neighb…