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I am a first-year Ph.D. student in Stanford Vision and Learning Lab (SVL) at the Stanford University. I'm currently rotating with Prof. Fei-Fei Li and Prof. Jiajun Wu. I received my Bachelor's degree from The University of Sydney, where I was fortunate to work with Prof. Weidong Cai. I was awarded Honors Class I and The University Medal. I have particular research interests on Machine Learning & Computer Vision. Contact: {X @ Y}, where X=xtiange, Y=stanford.edu |
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Tiange Xiang, Yixiao Zhang, Yongyi Lu, Alan L. Yuille, Chaoyi Zhang, Weidong Cai, and Zongwei Zhou IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 We re-formulated unsupervised anomaly detection for radiography images as semantic-sapce in-painting. [ paper / code ] |
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Zijiao Chen*, Jiaxin Qing*, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 We decoded photo-realistic visual stimuli from fMRI brain signals. [ project page / code / paper ] |
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Tiange Xiang, Mahmut Yurt, Ali B Syed, Kawin Setsompop, and Akshay Chaudhari International Conference on Learning Representations (ICLR), 2023 We achieved self-supervised MRI denoising through generative diffusion models. [ paper / code ] |
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Tiange Xiang, Chaoyi Zhang, Yang Song, Jianhui Yu, and Weidong Cai IEEE/CVF International Conference on Computer Vision (ICCV), 2021 We proposed a geometry-aware feature aggregation operator for point cloud analysis. SOTA performances on multiple fundamental benchmarks. [ project page / paper / code ] |
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Tiange Xiang, Chaoyi Zhang, Xinyi Wang, Yang Song, Dongnan Liu, Heng Huang, and Weidong Cai Medical Image Analysis, 2022 A very efficient and light-weight encoder-decoder network that achieved SOTA performances on multiple 2D and 3D medical image segmentation benchmarks. [ project page / paper ] |
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Xinyi Wang*, Tiange Xiang*, Chaoyi Zhang, Yang Song, Dongnan Liu, Heng Huang, and Weidong Cai International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021 We proposed a two-phase NAS method to search a super efficient bi-directional architecture for medical image segmentation. [ project page / paper / code ] |
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Tiange Xiang, Chaoyi Zhang, Dongnan Liu, Yang Song, Heng Huang, and Weidong Cai International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020 We proposed bi-directional skip connections in the encoder-decoder architecture. Better performances can be achived without increasing network parameter. [ project page / paper / code ] |
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Tiange Xiang, Yang Song, Chaoyi Zhang, Dongnan Liu, Mei Chen, Fan Zhang, Heng Huang, Lauren O'Donnell, and Weidong Cai IEEE Transactions on Medical Imaging, 2022 We proposed an attention-based network to combine global-local visual clues for weakly-supervised WSI analysis. [ paper ] |
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Tiange Xiang, Chaoyi Zhang, Yang Song, Siqi Liu, We formulated network regularization as generative distortions through learnable graph reasoning modules. [ project page / paper ] |
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Tiange Xiang, We achieved real-time denoising for online rendering at adaptive spp counts. Multiple novel operators were designed in our two-phase denoising framework. [ paper ] |
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