DETRs learn an optimal specialist strategy via the Hungarian loss, motivating the new Object-level Calibration Error (OCE) metric and an image-level post-hoc uncertainty quantification framework.
Microsoft coco: Common objects in context
2 Pith papers cite this work. Polarity classification is still indexing.
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A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
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Uncertainty Quantification in Detection Transformers: Object-Level Calibration and Image-Level Reliability
DETRs learn an optimal specialist strategy via the Hungarian loss, motivating the new Object-level Calibration Error (OCE) metric and an image-level post-hoc uncertainty quantification framework.
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Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.