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Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training.
The hungarian method for the assignment problem
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Feature pyramid networks for object detection
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Focal loss for dense object detection
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Multi-scale positive sample refinement for few-shot object detection
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Deformable detr: Deformable transformers for end-to-end object detection
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Generalized intersection over union: A metric and a loss for bounding box regression
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Droploss for long-tail instance segmentation
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Seesaw loss for long-tailed instance segmentation
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Meta-detr: Few-shot object detection via unified image-level meta-learning
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Adaptive hierarchical representation learning for long-tailed object detection
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Equalized focal loss for dense long-tailed object detection
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