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20202023
most citedControlVideo: Training-free Controllable Text-to-Video Generation

34 citations · 69 across the 9 of their papers we have counts for

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10 papers · 1 filter

cs.CV202334 cited

ControlVideo: Training-free Controllable Text-to-Video Generation

Yabo Zhang, Yuxiang Wei, Dongsheng Jiang +3

Text-driven diffusion models have unlocked unprecedented abilities in image generation, whereas their video counterpart still lags behind due to the excessive training cost of temp…

cs.CV20231 cited

ShiftDDPMs: Exploring Conditional Diffusion Models by Shifting Diffusion Trajectories

Zijian Zhang, Zhou Zhao, Jun Yu +1

Diffusion models have recently exhibited remarkable abilities to synthesize striking image samples since the introduction of denoising diffusion probabilistic models (DDPMs). Their…

cs.CV2023

M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios

Ning Liao, Xiaopeng Zhang, Min Cao +1

In realistic open-set scenarios where labels of a part of testing data are totally unknown, when vision-language (VL) prompt learning methods encounter inputs related to unknown cl…

cs.CV2023

Rethinking Visual Prompt Learning as Masked Visual Token Modeling

Ning Liao, Bowen Shi, Xiaopeng Zhang +3

Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as th…

cs.CV20224 cited

ConfounderGAN: Protecting Image Data Privacy with Causal Confounder

Qi Tian, Kun Kuang, Kelu Jiang +3

The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet. However, it also means that users' private data may be co…

cs.CV2022

See Blue Sky: Deep Image Dehaze Using Paired and Unpaired Training Images

Xiaoyan Zhang, Gaoyang Tang, Yingying Zhu +1

The issue of image haze removal has attracted wide attention in recent years. However, most existing haze removal methods cannot restore the scene with clear blue sky, since the co…