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20182022
most citedSegmentation-Aware Image Denoising without Knowing True Segmentation

15 citations · 22 across the 3 of their papers we have counts for

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5 papers · 1 filter

cs.CV20226 cited

Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization

Junru Wu, Yi Liang, Feng Han +3

Self-supervised pre-training recently demonstrates success on large-scale multimodal data, and state-of-the-art contrastive learning methods often enforce the feature consistency f…

cs.CV20221 cited

Grasping the Arrow of Time from the Singularity: Decoding Micromotion in Low-dimensional Latent Spaces from StyleGAN

Qiucheng Wu, Yifan Jiang, Junru Wu +5

The disentanglement of StyleGAN latent space has paved the way for realistic and controllable image editing, but does StyleGAN know anything about temporal motion, as it was only t…

cs.CV2019

DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better

Orest Kupyn, Tetiana Martyniuk, Junru Wu +1

We present a new end-to-end generative adversarial network (GAN) for single image motion deblurring, named DeblurGAN-v2, which considerably boosts state-of-the-art deblurring effic…

cs.CV201915 cited

Segmentation-Aware Image Denoising without Knowing True Segmentation

Sicheng Wang, Bihan Wen, Junru Wu +2

Several recent works discussed application-driven image restoration neural networks, which are capable of not only removing noise in images but also preserving their semantic-aware…

cs.CV2019

Bridging the Gap Between Computational Photography and Visual Recognition

Rosaura G. VidalMata, Sreya Banerjee, Brandon RichardWebster +21

What is the current state-of-the-art for image restoration and enhancement applied to degraded images acquired under less than ideal circumstances? Can the application of such algo…