Search Results for author: Chenglong Ma

Found 10 papers, 5 papers with code

Prompted Contextual Transformer for Incomplete-View CT Reconstruction

1 code implementation13 Dec 2023 Chenglong Ma, Zilong Li, Junjun He, Junping Zhang, Yi Zhang, Hongming Shan

To enjoy the multi-setting synergy in a single model, we propose a novel Prompted Contextual Transformer (ProCT) for incomplete-view CT reconstruction.

Computed Tomography (CT)

Prompt-In-Prompt Learning for Universal Image Restoration

1 code implementation8 Dec 2023 Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, Hongming Shan

Second, we devise a novel prompt-to-prompt interaction module to fuse these two prompts into a universal restoration prompt.

Deblurring Image Denoising +2

Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion Recognition

1 code implementation4 Aug 2023 Jiaxin Ye, Yujie Wei, Xin-Cheng Wen, Chenglong Ma, Zhizhong Huang, KunHong Liu, Hongming Shan

On one hand, our contrastive emotion decoupling achieves decoupling learning via a contrastive decoupling loss to strengthen the separability of emotion-relevant features from corpus-specific ones.

Cross-corpus Speech Emotion Recognition +1

FreeSeed: Frequency-band-aware and Self-guided Network for Sparse-view CT Reconstruction

1 code implementation12 Jul 2023 Chenglong Ma, Zilong Li, Junping Zhang, Yi Zhang, Hongming Shan

Specifically, we first propose a frequency-band-aware artifact modeling network (FreeNet), which learns artifact-related frequency-band attention in Fourier domain for better modeling the globally distributed streak artifact on the sparse-view CT images.

Computed Tomography (CT)

OrthoGAN:High-Precision Image Generation for Teeth Orthodontic Visualization

no code implementations29 Dec 2022 Feihong Shen, Jingjing Liu, Haizhen Li, Bing Fang, Chenglong Ma, Jin Hao, Yang Feng, Youyi Zheng

We design a multi-modal encoder-decoder based generative model to synthesize identity-preserving frontal facial images with aligned teeth.

Decoder Image Generation

An evaluation of U-Net in Renal Structure Segmentation

no code implementations6 Sep 2022 Haoyu Wang, Ziyan Huang, Jin Ye, Can Tu, Yuncheng Yang, Shiyi Du, Zhongying Deng, Chenglong Ma, Jingqi Niu, Junjun He

Renal structure segmentation from computed tomography angiography~(CTA) is essential for many computer-assisted renal cancer treatment applications.

Image Segmentation Medical Image Segmentation +2

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