activity
20232025
most citedLearning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models

Lingshun Kong, Jiawei Zhang, Dongqing Zou +4

Diffusion models have achieved significant progress in image generation. The pre-trained Stable Diffusion (SD) models are helpful for image deblurring by providing clear image prio…

cs.CV2024

Generative Inbetweening through Frame-wise Conditions-Driven Video Generation

Tianyi Zhu, Dongwei Ren, Qilong Wang +2

Generative inbetweening aims to generate intermediate frame sequences by utilizing two key frames as input. Although remarkable progress has been made in video generation models, g…

cs.CV2024

Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention

Hannan Lu, Xiaohe Wu, Shudong Wang +5

Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Exist…

physics.plasm-ph2024

Reconstruction of Poloidal Magnetic Fluxes on EAST based on Neural Networks with Measured Signals

Feifei Long, Xiangze Xia, Jian Liu +9

The accurate construction of tokamak equilibria, which is critical for the effective control and optimization of plasma configurations, depends on the precise distribution of magne…

cs.CV20231 cited

Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

Qing Miao, Xiaohe Wu, Chao Xu +4

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supe…