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In this paper, we provide the observation that too few queries assigned as positive samples in DETR with one-to-one set matching leads to sparse supervision on the encoder's output which considerably hurt the discriminative feature learning of the encoder and vice visa for attention learning in the decoder.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Fast r-cnn
Ross Girshick · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
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Mask r-cnn
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Hybrid task cascade for instance segmentation
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Generalized intersection over union: A metric and a loss for bounding box regression
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End-to-end object detection with transformers
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Probabilistic anchor assignment with iou prediction for object detection
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Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
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