Huang, C., & Czarnecki, K. (2025). MFSeg: Efficient Multi-frame 3D Semantic Segmentation Presented at the IEEE International Conference on Robotics and Automation (ICRA) conference. Atlanta, GA, USA: IEEE. https://doi.org/10.1109/ICRA55743.2025.11127629 (Original work published 2025)
References
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2025
Tang, M. Q., Sedwards, S., Huang, C., & Czarnecki, K. (2025). How Hard is Snow? A Paired Domain Adaptation Dataset for Clear and Snowy Weather: CADC+ Presented at the IEEE Intelligent Vehicles Symposium (IV) conference. Cluj, Romania: IEEE. https://doi.org/10.1109/IV64158.2025.11097651 (Original work published 2025)
Wang, Y., & Czarnecki, K. (2025). AiDe: Improving 3D Open-Vocabulary Semantic Segmentation by Aligned Vision-Language Learning Presented at the IEEE/CVF/Winter/Conference/on/Applications/of/Computer/Vision/(WACV) conference. Tucson, AZ, USA: IEEE. https://doi.org/10.1109/WACV61041.2025.00265 (Original work published 2025)
Zhang, R., Sun, C., Ning, M., Valiollahimehrizi, R., Lu, Y., Czarnecki, K., & Khajepour, A. (2025). Quantifying Learning Algorithm Uncertainties in Autonomous Driving Systems: Enhancing Safety through Polynomial Chaos Expansion and High-Definition Maps Accident Analysis & Prevention, 211, 11. https://doi.org/10.1016/j.aap.2024.107903 (Original work published 2025)
Hu, B. C., Di Sandro, A., Marsso, L., Czarnecki, K., & Chechik, M. (2025). Arguing Reliability of Machine Learning-based Components Presented at the IEEE Engineering Reliable Autonomous Systems (ERAS) conference. Worcester, MA, USA: IEEE. https://doi.org/10.1109/ERAS63351.2025.11135634 (Original work published 2026)
Chow, A., Riddell, E., Wang, Y., Sedwards, S., & Czarnecki, K. (2025). OV-SCAN: Semantically Consistent Alignment for Novel Object Discovery in Open-Vocabulary 3D Object Detection Presented at the IEEE/CVF/International/Conference/on/Computer/Vision/(ICCV) conference. Honolulu, USA: Computer Vision Foundation. Retrieved from https://openaccess.thecvf.com/content/ICCV2025/html/Chow_OV-SCAN_Semantically_Consistent_Alignment_for_Novel_Object_Discovery_in_Open-Vocabulary_ICCV_2025_paper.html (Original work published 2025)
Wang, Y., Azadani, M. N., Sedwards, S., & Czarnecki, K. (2025). Hawaii: Hierarchical Visual Knowledge Transfer for Efficient Vision-Language Models Presented at the Advances in Neural Information Processing Systems (NeurIPS) conference. San Diego, USA: Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2025/file/018af723a54a6d3bc5706d3a3126abf0-Paper-Conference.pdf (Original work published 2025)
Zhang, R., Sun, C., Valiollahimehrizi, R., Czarnecki, K., & Khajepour, A. (2025). An Uncertainty-Aware, Dual-Tiered Decision-Making Method for Safe Autonomous Driving IEEE Transactions on Intelligent Transportation Systems, 26(1), 12. https://doi.org/10.1109/TITS.2024.3492794 (Original work published 2025)
Shen, H., Hu, B. C., Czarnecki, K., Marsso, L., & Chechik, M. (2025). Assessing Visually-Continuous Corruption Robustness of Neural Networks Relative to Human Performance Presented at the IEEE/CVF/Winter/Conference/on/Applications/of/Computer/Vision/(WACV) conference. Tucson, Arizona, USA: IEEE. https://doi.org/10.1109/WACV61041.2025.00614 (Original work published 2025)
2024
Huang, C., Abdelzad, V., Sedwards, S., & Czarnecki, K. (2024). SOAP: Cross-sensor Domain Adaptation for 3D Object Detection Using Stationary Object Aggregation Pseudo-labelling Presented at the 2024 IEEE CVF Winter Conference on Applications of Computer Vision (WACV) conference. Waikoloa, HI, USA: IEEE. https://doi.org/10.1109/WACV57701.2024.00332 (Original work published 2024)