44 citations · 141 across the 8 of their papers we have counts for
8 papers
Masked autoencoders are effective solution to transformer data-hungry
Jiawei Mao, Honggu Zhou, Xuesong Yin +1
Vision Transformers (ViTs) outperforms convolutional neural networks (CNNs) in several vision tasks with its global modeling capabilities. However, ViT lacks the inductive bias inh…
The Nuts and Bolts of Adopting Transformer in GANs
Rui Xu, Xiangyu Xu, Kai Chen +2
Transformer becomes prevalent in computer vision, especially for high-level vision tasks. However, adopting Transformer in the generative adversarial network (GAN) framework is sti…
Positional Encoding as Spatial Inductive Bias in GANs
Rui Xu, Xintao Wang, Kai Chen +2
SinGAN shows impressive capability in learning internal patch distribution despite its limited effective receptive field. We are interested in knowing how such a translation-invari…
CARAFE++: Unified Content-Aware ReAssembly of FEatures
Jiaqi Wang, Kai Chen, Rui Xu +3
Feature reassembly, i.e. feature downsampling and upsampling, is a key operation in a number of modern convolutional network architectures, e.g., residual networks and feature pyra…
Texture Memory-Augmented Deep Patch-Based Image Inpainting
Rui Xu, Minghao Guo, Jiaqi Wang +3
Patch-based methods and deep networks have been employed to tackle image inpainting problem, with their own strengths and weaknesses. Patch-based methods are capable of restoring a…
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
Minghao Guo, Yuzhe Yang, Rui Xu +2
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks. Since then, extensive efforts have been devoted to enhancing the robustne…
Deep Flow-Guided Video Inpainting
Rui Xu, Xiaoxiao Li, Bolei Zhou +1
Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video c…
CARAFE: Content-Aware ReAssembly of FEatures
Jiaqi Wang, Kai Chen, Rui Xu +3
Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as ob…