Shuo Lei

About Me

I am a research scientist at Sony Research. I received my Ph.D. in Computer Science at Virginia Tech under the supervision of Prof. Chang-Tien Lu. I received an M.Sc. degree and a B.A. degree in Software Engineering at Beihang University. My research interests are in the broad area of machine learning with a particular focus on learning with limited labeled data, including few-/zero-shot learning, uncertainty-aware modeling, and applications in computer vision and natural language processing tasks such as semantic segmentation and text classification.

News

• [Dec. 2025] One paper was accepted by Information Fusion.
• [Dec. 2024] One paper was accepted by NeurIPS 2024.
• [Jun. 2024] One paper was accepted by NAACL 2024.
• [Apr. 2024] One paper was accepted by ICASSP 2024.
• [Nov. 2023] One paper was accepted by SIGGRAPH 2023.
• [Aug. 2023] One paper was accepted by KDD 2023.
• [May 2023] One paper was accepted by ACL 2023.
• [Jul. 2022] One paper was accepted by ECCV 2022.
• [Jun. 2022] One paper was accepted by Neurocomputing.
• [May 2022] One paper was accepted by NAACL 2022.

Publications

• [NeurIPS 24] Dc-gaussian: Improving 3d gaussian splatting for reflective dash cam videos
  Linhan Wang, Kai Cheng, Shuo Lei, Shengkun Wang, Wei Yin, Chenyang Lei, Xiaoxiao Long, Chang-Tien Lu.
  Advances in Neural Information Processing Systems 37 (2024). [paper] [code]
  

• [NAACL 24] Uncertainty estimation on sequential labeling via uncertainty transmission
  Jianfeng He, Linlin Yu, Shuo Lei, Chang-Tien Lu, Feng Chen.
  In Findings of the Association for Computational Linguistics: NAACL 2024. [paper] [code]
  

• [ACL’23] TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
  Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen and Chang-Tien Lu.
  The 61st Annual Meeting of the Association for Computational Linguistics(ACL), Toronto, Canada, July 9-14, 2023. [paper] [code]
  

• [ECCV’22] Cross-Domain Few-Shot Semantic Segmentation
  Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Bowen Du and Chang-Tien Lu
  European Conference on Computer Vision 2022 (ECCV), Tel-Aviv, Israel, Oct. 23-27, 2022. [paper] [code]
  

• [NAACL’22] Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels
  Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen and Chang-Tien Lu
  Annual Conference of the North American Chapter of the Association for Computational Linguistics (Findings of NAACL’22), Seattle, Washington, USA, July 2022.[paper] [code]
  

• [Neurocomputing’22] Semantic Inpainting on Segmentation Map via Multi-Expansion Loss
  Jianfeng He, Xuchao Zhang, Shuo Lei, Shuhui Wang, Chang-Tien Lu and Bei Xiao
  Neurocomputing, Elsevier, 2022
  

• [TKDD] Online and Distributed Robust Regressions with Extremely Noisy Labels
  Shuo Lei, Xuchao Zhang, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu
  ACM Transactions on Knowledge Discovery from Data (TKDD), 2021. [paper]
  

• [ICME’21] Few-Shot Semantic Segmenation via Prototype Augmentation with Image-level Annotations
  Shuo Lei, Xuchao Zhang, Jianfeng He, Fanglan Chen, Bowen Du and Chang-Tien Lu
  IEEE International Conference on Multimedia and Expo (ICME), Virtual, Shenzhen, July 5–9, 2021 [paper]
  

• [ICCVW’21] Reducing Noise Pixels and Metric Bias in Semantic Inpainting on Segmentation Map
  Jianfeng He, Bei Xiao, Xuchao Zhang, Shuo Lei, Shuhui Wang, Chang-Tien Lu
  IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) 2021. [paper]
  

• [EMNLP’20] Towards More Accurate Uncertainty Estimation In Text Classification
  Jianfeng He, Xuchao Zhang, Shuo Lei, Zhiqian Chen, Fanglan Chen, Abdulaziz Alhamadani, Bei Xiao and ChangTien Lu
  2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Nov. 16-20, 2020. [paper] [code]
  

• [SIGSPATIAL’20] Graph Convolutional Networks with Kalman Filtering for Traffic Prediction
  Fanglan Chen, Zhiqian Chen, Subhodip Biswas, Shuo Lei, Naren Ramakrishnan, Chang-Tien Lu
  ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL) 2020. [paper] [code]
  

• [TKDD] Robust Regression via Heuristic Corruption Thresholding and Its Adaptive Estimation Variation
  Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu
  ACM Transactions on Knowledge Discovery from Data (TKDD), 2019. [paper]
  

• [ICDM’18] Robust Regression via Online Feature Selection under Adversarial Data Corruption
  Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boedihardjo, Chang-Tien Lu
  Proceedings of the IEEE International Conference on Data Mining (ICDM), Singapore, Nov. 17-20, 2018. [paper]
  

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