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cs.CV2023

SynFacePAD 2023: Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data

Meiling Fang, Marco Huber, Julian Fierrez +21

This paper presents a summary of the Competition on Face Presentation Attack Detection Based on Privacy-aware Synthetic Training Data (SynFacePAD 2023) held at the 2023 Internation…

cs.CV20231 cited

Dynamic Association Learning of Self-Attention and Convolution in Image Restoration

Kui Jiang, Xuemei Jia, Wenxin Huang +3

CNNs and Self attention have achieved great success in multimedia applications for dynamic association learning of self-attention and convolution in image restoration. However, CNN…

cs.CV202316 cited

Super-Resolving Face Image by Facial Parsing Information

Chenyang Wang, Junjun Jiang, Zhiwei Zhong +2

Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. In this paper, we build a novel parsing map guided fac…

cs.CV20237 cited

Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution

Gang Wu, Junjun Jiang, Yuanchao Bai +1

In recent years, the use of large convolutional kernels has become popular in designing convolutional neural networks due to their ability to capture long-range dependencies and pr…

cs.CV20231 cited

Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection

Zhenyu Li, Zhipeng Zhang, Heng Fan +4

In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework. Specifically, having observed that the bottleneck of this…

cs.CV202360 cited

Guided Depth Map Super-resolution: A Survey

Zhiwei Zhong, Xianming Liu, Junjun Jiang +2

Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image…