Search Results for author: Xiaoyan Luo

Found 6 papers, 2 papers with code

Fusion-Mamba for Cross-modality Object Detection

no code implementations14 Apr 2024 Wenhao Dong, Haodong Zhu, Shaohui Lin, Xiaoyan Luo, Yunhang Shen, Xuhui Liu, Juan Zhang, Guodong Guo, Baochang Zhang

In this paper, we investigate cross-modality fusion by associating cross-modal features in a hidden state space based on an improved Mamba with a gating mechanism.

Object object-detection +1

Neural optimal controller for stochastic systems via pathwise HJB operator

no code implementations23 Feb 2024 Zhe Jiao, Xiaoyan Luo, Xinlei Yi

The aim of this work is to develop deep learning-based algorithms for high-dimensional stochastic control problems based on physics-informed learning and dynamic programming.

GMTalker: Gaussian Mixture based Emotional talking video Portraits

no code implementations12 Dec 2023 Yibo Xia, Lizhen Wang, Xiang Deng, Xiaoyan Luo, Yebin Liu

Specifically, we propose a Gaussian Mixture based Expression Generator (GMEG) which can construct a continuous and multi-modal latent space, achieving more flexible emotion manipulation.

RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray Projection

1 code implementation12 Oct 2023 Miao Zhu, Qiming Fu, Bo Liu, Mengxi Zhang, Bojian Li, Xiaoyan Luo, Fugen Zhou

In this study, a novel imaging method RT-SRTS is proposed which integrates 3D imaging and tumor segmentation into one network based on multi-task learning (MTL) and achieves real-time simultaneous 3D reconstruction and tumor segmentation from a single X-ray projection at any angle.

3D Reconstruction Multi-Task Learning +2

Implicit Diffusion Models for Continuous Super-Resolution

1 code implementation CVPR 2023 Sicheng Gao, Xuhui Liu, Bohan Zeng, Sheng Xu, Yanjing Li, Xiaoyan Luo, Jianzhuang Liu, XianTong Zhen, Baochang Zhang

IDM integrates an implicit neural representation and a denoising diffusion model in a unified end-to-end framework, where the implicit neural representation is adopted in the decoding process to learn continuous-resolution representation.

Denoising Image Super-Resolution

Inference-InfoGAN: Inference Independence via Embedding Orthogonal Basis Expansion

no code implementations2 Oct 2021 Hongxiang Jiang, Jihao Yin, Xiaoyan Luo, Fuxiang Wang

To ensure the target-wise interpretable representation, we add a consistence constraint between the expansion coefficients and latent variables on the base of MI maximization.

Attribute Disentanglement

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