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20172024
most citedRTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

76 citations · 499 across the 41 of their papers we have counts for

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Showing 2022 · cs.CVShow all

14 papers · 2 filters

cs.CV2022★ 1 cited

Cyclically Disentangled Feature Translation for Face Anti-spoofing

Haixiao Yue, Keyao Wang, Guosheng Zhang +4

Current domain adaptation methods for face anti-spoofing leverage labeled source domain data and unlabeled target domain data to obtain a promising generalizable decision boundary.…

cs.CV2022★ 8 cited

CAE v2: Context Autoencoder with CLIP Target

Xinyu Zhang, Jiahui Chen, Junkun Yuan +10

Masked image modeling (MIM) learns visual representation by masking and reconstructing image patches. Applying the reconstruction supervision on the CLIP representation has been pr…

cs.CV2022★ 19 cited

Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining

Qiang Chen, Jian Wang, Chuchu Han +12

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dos…

cs.CV2022★ 3 cited

KD-DETR: Knowledge Distillation for Detection Transformer with Consistent Distillation Points Sampling

Yu Wang, Xin Li, Shengzhao Weng +5

DETR is a novel end-to-end transformer architecture object detector, which significantly outperforms classic detectors when scaling up. In this paper, we focus on the compression o…

cs.CV2022★ 76 cited

RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer

Jian Wang, Chenhui Gou, Qiman Wu +4

Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in th…

cs.CV2022★ 1 cited

StyleSwap: Style-Based Generator Empowers Robust Face Swapping

Zhiliang Xu, Hang Zhou, Zhibin Hong +7

Numerous attempts have been made to the task of person-agnostic face swapping given its wide applications. While existing methods mostly rely on tedious network and loss designs, t…