Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
- DOI
- 10.1109/tpami.2026.3686715
- Published
- 2026-11
- Container
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tpami.2026.3686715,
title = {Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability},
author = {Young-Jin Park and Carson Sobolewski and Navid Azizan},
year = {2026},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
doi = {10.1109/tpami.2026.3686715},
url = {https://doi.org/10.1109/tpami.2026.3686715}
}RIS
TY - JOUR TI - Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability AU - Young-Jin Park AU - Carson Sobolewski AU - Navid Azizan PY - 2026 JO - IEEE Transactions on Pattern Analysis and Machine Intelligence DO - 10.1109/tpami.2026.3686715 UR - https://doi.org/10.1109/tpami.2026.3686715 ER -
APA
Park, Y., Sobolewski, C., & Azizan, N. (2026). Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2026.3686715
Source records
- crossref · retrieved 2026-11-10T15:54:06.032Z