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On Calibration of Object Detectors: Pitfalls, Evaluation and Baselines.

, , , и . CoRR, (2024)

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Yücel Öksüz

Mukaddes Öksüz

Mevlüt Kemal

Ayten Öksüz

Ali Kemal

 

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Imbalance Problems in Object Detection: A Review, , , и . (2019)cite arxiv:1909.00169Comment: Accepted to IEEE TPAMI; currently in press.Correlation Loss: Enforcing Correlation between Classification and Localization., , , и . AAAI, стр. 1087-1095. AAAI Press, (2023)Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration., , и . CVPR, стр. 9263-9274. IEEE, (2023)Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation., , , , и . CoRR, (2023)Rank & Sort Loss for Object Detection and Instance Segmentation., , , и . ICCV, стр. 2989-2998. IEEE, (2021)One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks., , , и . CoRR, (2020)A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection., , , и . NeurIPS, (2020)Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation., , , , , и . BMVC, стр. 228. BMVA Press, (2021)Identifying and addressing imbalance problems in visual detection (Görsel tespitteki dengesizlik problemlerinin belirlenmesi ve çözümlenmesi). Middle East Technical University, Turkey, (2021)base-search.net (ftmetuankair:oai:https://open.metu.edu.tr:11511/90919).Generating Positive Bounding Boxes for Balanced Training of Object Detectors., , , и . WACV, стр. 883-892. IEEE, (2020)