Search Results for author: Zhe Kong

Found 5 papers, 3 papers with code

StereoCrafter: Diffusion-based Generation of Long and High-fidelity Stereoscopic 3D from Monocular Videos

no code implementations11 Sep 2024 Sijie Zhao, WenBo Hu, Xiaodong Cun, Yong Zhang, Xiaoyu Li, Zhe Kong, Xiangjun Gao, Muyao Niu, Ying Shan

This paper presents a novel framework for converting 2D videos to immersive stereoscopic 3D, addressing the growing demand for 3D content in immersive experience.

Video Inpainting

OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models

1 code implementation16 Mar 2024 Zhe Kong, Yong Zhang, Tianyu Yang, Tao Wang, Kaihao Zhang, Bizhu Wu, GuanYing Chen, Wei Liu, Wenhan Luo

We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.

Denoising Text-to-Image Generation

Dual Teacher Knowledge Distillation with Domain Alignment for Face Anti-spoofing

no code implementations2 Jan 2024 Zhe Kong, Wentian Zhang, Tao Wang, Kaihao Zhang, Yuexiang Li, Xiaoying Tang, Wenhan Luo

In this paper, we propose a domain adversarial attack (DAA) method to mitigate the training instability problem by adding perturbations to the input images, which makes them indistinguishable across domains and enables domain alignment.

Adversarial Attack Face Anti-Spoofing +2

Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising

1 code implementation15 Nov 2021 Feng Liu, Zhe Kong, Haozhe Liu, Wentian Zhang, Linlin Shen

The proposed method learns important features of fingerprint images by weighing the importance of each channel and identifying discriminative channels and "noise" channels.

Denoising

Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing

1 code implementation9 Sep 2021 Zhe Kong, Wentian Zhang, Feng Liu, Wenhan Luo, Haozhe Liu, Linlin Shen, Raghavendra Ramachandra

Even though there are numerous Presentation Attack Detection (PAD) techniques based on both deep learning and hand-crafted features, the generalization of PAD for unknown PAI is still a challenging problem.

Self-Supervised Learning

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