Yongyi Su

Orcid: 0009-0001-6911-8256

According to our database1, Yongyi Su authored at least 18 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
AD-FM: Multimodal LLMs for Anomaly Detection via Multi-Stage Reasoning and Fine-Grained Reward Optimization.
CoRR, August, 2025

SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation.
CoRR, June, 2025

Robust Distribution Alignment for Industrial Anomaly Detection under Distribution Shift.
CoRR, March, 2025

PointSAM: Pointly-Supervised Segment Anything Model for Remote Sensing Images.
IEEE Trans. Geosci. Remote. Sens., 2025

Mitigating Missing Feature Channels at Inference Stage: Test-Time Adaptation Through Self-Training With Data Imputation.
IEEE Signal Process. Lett., 2025

Bidirectional Segmentation-Aware Network for One-Shot Object Detection.
Neurocomputing, 2025

Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering Regularized Self-Training.
IEEE Trans. Pattern Anal. Mach. Intell., August, 2024

Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model Selection.
Trans. Mach. Learn. Res., 2024

Clip-Guided Source-Free Object Detection in Aerial Images.
Proceedings of the IGARSS 2024, 2024

Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Towards Real-World Test-Time Adaptation: Tri-net Self-Training with Balanced Normalization.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning.
IEEE Trans. Circuits Syst. Video Technol., December, 2023

STFAR: Improving Object Detection Robustness at Test-Time by Self-Training with Feature Alignment Regularization.
CoRR, 2023

On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022


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