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20182024
most citedPretraining is All You Need for Image-to-Image Translation

92 citations · 303 across the 28 of their papers we have counts for

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26 papers · 1 filter

cs.CV2021

Exploring Temporal Coherence for More General Video Face Forgery Detection

Yinglin Zheng, Jianmin Bao, Dong Chen +2

Although current face manipulation techniques achieve impressive performance regarding quality and controllability, they are struggling to generate temporal coherent face videos. I…

cs.CV2021

Dual Path Learning for Domain Adaptation of Semantic Segmentation

Yiting Cheng, Fangyun Wei, Jianmin Bao +3

Domain adaptation for semantic segmentation enables to alleviate the need for large-scale pixel-wise annotations. Recently, self-supervised learning (SSL) with a combination of ima…

cs.CV2021

Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation

Weilun Wang, Wengang Zhou, Jianmin Bao +2

Contrastive learning shows great potential in unpaired image-to-image translation, but sometimes the translated results are in poor quality and the contents are not preserved consi…

cs.CV20215 cited

High-Fidelity and Arbitrary Face Editing

Yue Gao, Fangyun Wei, Jianmin Bao +4

Cycle consistency is widely used for face editing. However, we observe that the generator tends to find a tricky way to hide information from the original image to satisfy the cons…

cs.CV2021

Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

Chulin Xie, Chuxin Wang, Bo Zhang +3

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv…

cs.CV202136 cited

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation

Pan Zhang, Bo Zhang, Ting Zhang +3

Self-training is a competitive approach in domain adaptive segmentation, which trains the network with the pseudo labels on the target domain. However inevitably, the pseudo labels…