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Imbalance Problems in Object Detection: A Review, , , and . (2019)cite arxiv:1909.00169Comment: Accepted to IEEE TPAMI; currently in press.Correlation Loss: Enforcing Correlation between Classification and Localization., , , and . AAAI, page 1087-1095. AAAI Press, (2023)Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration., , and . CVPR, page 9263-9274. IEEE, (2023)One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks., , , and . CoRR, (2020)Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation., , , , and . CoRR, (2023)Rank & Sort Loss for Object Detection and Instance Segmentation., , , and . ICCV, page 2989-2998. IEEE, (2021)A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection., , , and . NeurIPS, (2020)Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation., , , , , and . BMVC, page 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., , , and . WACV, page 883-892. IEEE, (2020)