Reference author: Krzysztof Czarnecki

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)

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)

Wang, Y., Azadani, M. N., Sedwards, S., & Czarnecki, K. (2025). Leo-mini: An efficient multimodal large language model using conditional token reduction and mixture of multi-modal experts Presented at the Conference on Empirical Methods in Natural Language Processing (EMNLP) conference. Suzhou, China. https://doi.org/10.18653/v1/2025.emnlp-main.368 (Original work published 2025)

Wang, Y., Riddell, E., Chow, A., Sedwards, S., & Czarnecki, K. (2026). Mitigating the modality gap: Few-shot out-of-distribution detection with multi-modal prototypes and image bias estimation Presented at the IEEE/CVF/Winter/Conference/on/Applications/of/Computer/Vision conference. Tucson, Arizona, USA. Retrieved from https://openreview.net/pdf?id=tgnTVmRybs

Azadani, M. N., Riddell, J., Sedwards, S., & Czarnecki, K. (2026). Rethinking the Mixture of Vision Encoders Paradigm for Enhanced Visual Understanding in Multimodal LLMs Transactions on Machine Learning Research. Retrieved from https://openreview.net/pdf?id=tgnTVmRybs (Original work published 2026)

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)

Riddell, E., Riddell, J., Sun, G., Antkiewicz, M., & Czarnecki, K. (2026). Stalled, Biased, and Confused: Uncovering Reasoning Failures in LLMs for Cloud-Based Root Cause Analysis Presented at the International Conference on AI Foundation Models and Software Engineering (FORGE) conference. https://doi.org/10.1145/3793655.3793732 (Original work published 2026)

Berger, T., Mahmoud, W., Zahra, R. A., Vassilevski, I., Burger, A., Ji, W., … Czarnecki, K. (2025). Cost and Benefit of Tracing Features with Embedded Annotations ACM Transactions on Software Engineering and Methodology, 35(4). https://doi.org/10.1145/3746060 (Original work published 2026)

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)

Huang, C., Abdelzad, V., Sedwards, S., & Czarnecki, K. (2025). VADet: Multi-Frame LiDAR 3D Object Detection Using Variable Aggregation Presented at the 2025 IEEE CVF Winter Conference on Applications of Computer Vision (WACV) conference. Tucson, AZ, USA: IEEE. https://doi.org/10.1109/WACV61041.2025.00079 (Original work published 2025)