Huang X, Liu M Y, Belongie S, et al. Multimodal unsupervised image-to-image translation[C]//Proceedings of the European Conference on Computer Vision (ECCV). 2018: 172-189.
(ICCV 2017) Arbitrary style transfer in real-time with adaptive instance normalization
Huang X, Belongie S. Arbitrary style transfer in real-time with adaptive instance normalization[C]//Proceedings of the IEEE International Conference on Computer Vision. 2017: 1501-1510.
(2019) Few-Shot Unsupervised Image-to-Image Translation
Liu M Y, Huang X, Mallya A, et al. Few-shot unsupervised image-to-image translation[J]. arXiv preprint arXiv:1905.01723, 2019.
(ICCV 2019 Best Paper) SinGAN:Learning a Generative Model from a Single Natural Image
Shaham T R, Dekel T, Michaeli T. Singan: Learning a generative model from a single natural image[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 4570-4580.
(2019) PSGAN:Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer
Keyword [Facial Landmarks] [Makeup Transfer]
Jiang W, Liu S, Gao C, et al. PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer[J]. arXiv preprint arXiv:1909.06956, 2019.
(ICCV 2018) Ccnet:Criss-cross attention for semantic segmentation
Keyword [Segmentation] [Recurrent Criss-cross Attention Module]
Huang Z, Wang X, Huang L, et al. Ccnet: Criss-cross attention for semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision. 2019: 603-612.
(2019) GCNet:Non-local Networks Meet Squeeze-Excitation Networks and Beyond
Keyword [Global Context Block]
Cao Y, Xu J, Lin S, et al. GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond[J]. arXiv preprint arXiv:1904.11492, 2019.
(NIPS 2018) Compact Generalized Non-local Network
Keyword [Compact Generalized Non-local]
Yue K, Sun M, Yuan Y, et al. Compact Generalized Non-local Network[C]. neural information processing systems, 2018: 6510-6519.
(2019) Randaugment:Practical data augmentation with no separate search
Cubuk E D, Zoph B, Shlens J, et al. RandAugment: Practical data augmentation with no separate search[J]. arXiv preprint arXiv:1909.13719, 2019.
(ICLR 2019) Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Keyword [Stylized-ImageNet]
Geirhos R, Rubisch P, Michaelis C, et al. ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness[C]. international conference on learning representations, 2019.