10 citations · 27 across the 6 of their papers we have counts for
7 papers · 1 filter
Be Careful with Rotation: A Uniform Backdoor Pattern for 3D Shape
Linkun Fan, Fazhi He, Qing Guo +3
For saving cost, many deep neural networks (DNNs) are trained on third-party datasets downloaded from internet, which enables attacker to implant backdoor into DNNs. In 2D domain,…
Decoupled Mixup for Generalized Visual Recognition
Haozhe Liu, Wentian Zhang, Jinheng Xie +7
Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models oft…
Class-Level Confidence Based 3D Semi-Supervised Learning
Zhimin Chen, Longlong Jing, Liang Yang +2
Recent state-of-the-art method FlexMatch firstly demonstrated that correctly estimating learning status is crucial for semi-supervised learning (SSL). However, the estimation metho…
Learning Scene Flow in 3D Point Clouds with Noisy Pseudo Labels
Bing Li, Cheng Zheng, Guohao Li +1
We propose a novel scene flow method that captures 3D motions from point clouds without relying on ground-truth scene flow annotations. Due to the irregularity and sparsity of poin…
Multimodal Semi-Supervised Learning for 3D Objects
Zhimin Chen, Longlong Jing, Yang Liang +2
In recent years, semi-supervised learning has been widely explored and shows excellent data efficiency for 2D data. There is an emerging need to improve data efficiency for 3D task…
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Bing Li, Yuanlue Zhu, Yitong Wang +3
In this paper, we propose a novel framework to translate a portrait photo-face into an anime appearance. Our aim is to synthesize anime-faces which are style-consistent with a give…