269 citations · 361 across the 46 of their papers we have counts for
46 papers
CineWeaver: Training-Free Reference-Controllable Multi-Shot Long Video Generation for Cinematic Storytelling
Yuyang Huang, Yabo Chen, Wenrui Dai +6
Cinematic video generation is challenging for text-to-video diffusion models due to concurrent requirements on multi-shot generation, fine-grained controllability over characters a…
Disease-Centric Vision-Language Pretraining with Hybrid Visual Encoding for 3D Computed Tomography
Bowen Shi, Weiwei Cao, Ruifeng Yuan +5
Vision-language pre-training (VLP) holds great promise for general-purpose medical AI by leveraging radiology reports as rich textual supervision, yet existing methods struggle wit…
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…
On the Nonasymptotic Bounds of Joint Source-Channel Coding with Hierarchical Sources
Shuo Shao, Chao Qi, Jincheng Dai +2
This paper establishes tractable bounds of joint source-channel coding with hierarchical sources in the finite blocklength regime. In this setting, both the indirect source and obs…
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).…
EV-NVC: Efficient Variable bitrate Neural Video Compression
Yongcun Hu, Yingzhen Zhai, Jixiang Luo +4
Training neural video codec (NVC) with variable rate is a highly challenging task due to its complex training strategies and model structure. In this paper, we train an efficient v…
Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation
Yuyang Huang, Yabo Chen, Junyu Zhou +5
Source-free domain adaptation (SFDA) is a challenging task that tackles domain shifts using only a pre-trained source model and unlabeled target data. Existing SFDA methods are res…
GranViT: A Fine-Grained Vision Model With Autoregressive Perception For MLLMs
Guanghao Zheng, Bowen Shi, Mingxing Xu +8
Vision encoders are indispensable for allowing impressive performance of Multi-modal Large Language Models (MLLMs) in vision language tasks such as visual question answering and re…
OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation
Han Li, Xinyu Peng, Yaoming Wang +7
We introduce OneCAT, a unified multimodal model that seamlessly integrates understanding, generation, and editing within a novel, pure decoder-only transformer architecture. Our fr…
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…
ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Yufeng Zhang, Wenrui Dai, Hang Yu +4
Neural Image Compression (NIC) has revolutionized image compression with its superior rate-distortion performance and multi-task capabilities, supporting both human visual percepti…
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…
LiftImage3D: Lifting Any Single Image to 3D Gaussians with Video Generation Priors
Yabo Chen, Chen Yang, Jiemin Fang +6
Single-image 3D reconstruction remains a fundamental challenge in computer vision due to inherent geometric ambiguities and limited viewpoint information. Recent advances in Latent…
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…
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Bowen Shi, Peisen Zhao, Zichen Wang +8
Vision-language foundation models, represented by Contrastive Language-Image Pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual…
Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification
Rui Yang, Wenrui Dai, Huajun She +3
Building comprehensive brain connectomes has proved of fundamental importance in resting-state fMRI (rs-fMRI) analysis. Based on the foundation of brain network, spatial-temporal-b…
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…