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20202026
most citedSpatial-Temporal Transformer Networks for Traffic Flow Forecasting

269 citations · 361 across the 46 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

cs.CV2024★ 1 cited

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…

cs.CV2024

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…