[Tiange :)]
Tiange Xiang 向天戈
Ph.D. Student, Stanford Vision and Learning Lab
Contact: {X @ Y}, X=xtiange, Y=stanford.edu
Biography
[more]
Tiange Xiang (向天戈) is a second-year CS Ph.D. student at Stanford University. He is affiliated with Stanford AI Lab & Stanford Vision and Learning Lab. He is advised by Prof. Fei-Fei Li, co-advised by Prof. Scott Delp and Prof. Ehsan Adeli. Previously, he worked with Prof. Weidong Cai at The University of Sydney, where he was awarded Honors Class I and The University Medal. His research focuses on 3D computer vision & AI for healthcare.
Name

Family name: 向(Xiàng) means the direction.

Given name: 天(Tiān) 戈(Gē) means the weapon from the heavenly realm.

Selected Conference Publications
[* indicates equal contribution]
[OccNeRF]
Rendering Humans from Object-Occluded Monocular Videos
Tiange Xiang, Adam Sun, Jiajun Wu, Ehsan Adeli, and Li Fei-Fei
[ICCV 2023] [Homepage] [Paper] [Code]
We rendered human from object occluded videos.
[SQUID]
SQUID: Deep Feature In-Painting for Unsupervised Anomaly Detection
Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, and Zongwei Zhou
[CVPR 2023] [Paper] [Code]
We re-formulated unsupervised anomaly detection as semantic-sapce in-painting.
[Mind-Vis]
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
Zijiao Chen*, Jiaxin Qing*, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou
[CVPR 2023] [Homepage] [Paper] [Code]
We decoded photo-realistic visual stimuli from fMRI brain signals.
[DDM2]
DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop, and Akshay Chaudhari
[ICLR 2023] [Paper] [Code]
We achieved self-supervised MRI denoising through generative diffusion models.
[CurveNet]
Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis
Tiange Xiang, Chaoyi Zhang, Yang Song, Jianhui Yu, and Weidong Cai
[ICCV 2021] [Homepage] [Paper] [Code]
We proposed a geometry-aware feature aggregation operator for point cloud analysis.
[BiX-NAS]
BiX-NAS: Searching Efficient Bi-directional Architectures for Medical Image Segmentation
Xinyi Wang*, Tiange Xiang*, Chaoyi Zhang, Yang Song, Dongnan Liu, Heng Huang, and Weidong Cai
[MICCAI 2021] [Homepage] [Paper] [Code]
We proposed a NAS method to search efficient bi-directional architectures.
[BiX-NAS]
BiO-Net: Learning Recurrent Bidirectional Connections for Encoder-Decoder Architecture
Tiange Xiang, Chaoyi Zhang, Dongnan Liu, Yang Song, Heng Huang, and Weidong Cai
[MICCAI 2020] [Homepage] [Paper] [Code]
We proposed bi-directional skip connections in the encoder-decoder architecture.
Journal Publications
[* indicates equal contribution]
[BiO-Nets]
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography Images
Tiange Xiang*, Yixiao Zhang*, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, and Zongwei Zhou
[TPAMI 2024] [Paper] [Code]
A faster and stronger network for chest X-ray anomaly detection.
[BiO-Nets]
Towards Bi-directional Skip Connections in Encoder-Decoder Architectures and Beyond
Tiange Xiang, Chaoyi Zhang, Xinyi Wang, Yang Song, Dongnan Liu, Heng Huang, and Weidong Cai
[MedIA 2022] [Homepage] [Paper]
An efficient and light-weight encoder-decoder network with SOTA performances.
[DSNet]
DSNet: A Weakly-Supervised Dual-Stream Framework for Effective Gigapixel Pathology Image Analysis
Tiange Xiang, Yang Song, Chaoyi Zhang, Dongnan Liu, Mei Chen, Fan Zhang, Heng Huang, Lauren O'Donnell, and Weidong Cai
[TMI 2022] [Paper]
We proposed to combine global-local clues for weakly-supervised WSI analysis.
Preprints
[* indicates equal contribution]
[dropgraph]
Partial Graph Reasoning for Neural Network Regularization
Tiange Xiang, Chaoyi Zhang, Yang Song, Siqi Liu, Hongliang Yuan, and Weidong Cai
[arXiv] [Paper]
[adapt]
Two-Stage Monte Carlo Denoising with Adaptive Sampling and Kernel Pool
Tiange Xiang, Hongliang Yuan, Haozhi Huang, and Yujin Shi
[arXiv] [Paper]
Education
Stanford University
Ph.D. Student in Computer Science
Palo Alto, CA, U.S.
Present
the University of Sydney
B.S. in Computer Science and Technology (Advanced) (Honours)
Sydney, NSW, Australia
July. 2022
Service
Conference reviewer:
CVPR (2021,2022,2023,2024), MICCAI (2021,2022,2023,2024), ICCV 2023, ICML 2022, ECCV (2022,2024), NeurIPS 2022, WACV 2023.
Jounral reviewer:
IEEE TPAMI, IEEE TIP, IEEE TMI, IEEE IV, Neurocomputing, Scientific Reports.
Teaching assistant:
CS 231n (Stanford, Spring 2024), CS 231n (Stanford, Spring 2023), COMP 3419 (USYD, Fall 2019)