no code implementations • 30 Dec 2024 • Min Zhang, Zilin Wang, Liyan Chen, KunHong Liu, Juncong Lin
Recent advances in AI-driven storytelling have enhanced video generation and story visualization.
no code implementations • 9 Dec 2024 • Zilin Wang, Shengqi Chen, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Guohua Wu, Yuan Tang, Jiayun Chen
Objective:To develop a no-reference image quality assessment method using automated distortion recognition to boost MRI-guided radiotherapy precision. Methods:We analyzed 106, 000 MR images from 10 patients with liver metastasis, captured with the Elekta Unity MR-LINAC. Our No-Reference Quality Assessment Model includes:1)image preprocessing to enhance visibility of key diagnostic features;2)feature extraction and directional analysis using MSCN coefficients across four directions to capture textural attributes and gradients, vital for identifying image features and potential distortions;3)integrative Quality Index(QI)calculation, which integrates features via AGGD parameter estimation and K-means clustering. The QI, based on a weighted MAD computation of directional scores, provides a comprehensive image quality measure, robust against outliers. LOO-CV assessed model generalizability and performance. Tumor tracking algorithm performance was compared with and without preprocessing to verify tracking accuracy enhancements. Results:Preprocessing significantly improved image quality, with the QI showing substantial positive changes and surpassing other metrics. After normalization, the QI's average value was 79. 6 times higher than CNR, indicating improved image definition and contrast. It also showed higher sensitivity in detail recognition with average values 6. 5 times and 1. 7 times higher than Tenengrad gradient and entropy. The tumor tracking algorithm confirmed significant tracking accuracy improvements with preprocessed images, validating preprocessing effectiveness. Conclusions:This study introduces a novel no-reference image quality evaluation method based on automated distortion recognition, offering a new quality control tool for MRIgRT tumor tracking. It enhances clinical application accuracy and facilitates medical image quality assessment standardization, with significant clinical and research value.
no code implementations • 12 Nov 2024 • Shengqi Chen, Zilin Wang, Jianrong Dai, Shirui Qin, Ying Cao, Ruiao Zhao, Jiayun Chen, Guohua Wu, Yuan Tang
This study aimed to enhance motion tracking precision in MRIgRT through an automatic real-time markerless tracking method using an enhanced Tracking-Learning-Detection (ETLD) framework with automatic segmentation.
1 code implementation • 31 May 2024 • Mingze Wang, Jinbo Wang, Haotian He, Zilin Wang, Guanhua Huang, Feiyu Xiong, Zhiyu Li, Weinan E, Lei Wu
In this work, we propose an Implicit Regularization Enhancement (IRE) framework to accelerate the discovery of flat solutions in deep learning, thereby improving generalization and convergence.
no code implementations • 18 Dec 2023 • Zilin Wang, Haolin Zhuang, Lu Li, Yinmin Zhang, Junjie Zhong, Jun Chen, Yu Yang, Boshi Tang, Zhiyong Wu
This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models.
no code implementations • 13 Oct 2023 • Lu Li, Yuxin Pan, RuoBing Chen, Jie Liu, Zilin Wang, Yu Liu, Zhiheng Li
Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations.
1 code implementation • 13 Sep 2023 • Sicheng Yang, Zilin Wang, Zhiyong Wu, Minglei Li, Zhensong Zhang, Qiaochu Huang, Lei Hao, Songcen Xu, Xiaofei Wu, Changpeng Yang, Zonghong Dai
The automatic co-speech gesture generation draws much attention in computer animation.
1 code implementation • 1 Jun 2023 • Lu Li, Jiafei Lyu, Guozheng Ma, Zilin Wang, Zhenjie Yang, Xiu Li, Zhiheng Li
Though normalization techniques have demonstrated huge success in supervised and unsupervised learning, their applications in visual RL are still scarce.
2 code implementations • 23 Mar 2022 • Jun Chen, Zilin Wang, Deyi Tuo, Zhiyong Wu, Shiyin Kang, Helen Meng
Previously proposed FullSubNet has achieved outstanding performance in Deep Noise Suppression (DNS) Challenge and attracted much attention.
1 code implementation • ICLR 2021 • Xavier Puig, Tianmin Shu, Shuang Li, Zilin Wang, Yuan-Hong Liao, Joshua B. Tenenbaum, Sanja Fidler, Antonio Torralba
In this paper, we introduce Watch-And-Help (WAH), a challenge for testing social intelligence in agents.
no code implementations • 23 Apr 2020 • Zengyuan Guo, Zilin Wang, Zhihui Wang, Wanli Ouyang, Haojie Li, Wen Gao
However, they are behind in accuracy comparing with recent segmentation-based text detectors.