MinerU2.5 uses a two-stage decoupled vision-language architecture to achieve state-of-the-art document parsing accuracy with lower computational overhead than existing general and domain-specific models.
Yolov10: Real-time end-to-end object detection.Advances in Neural Information Processing Systems, 37:107984–108011
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
A two-stage weakly supervised pipeline pretrains on auto-generated school labels from sparse points and fine-tunes on only 50 manual examples to achieve strong detection performance in aerial imagery.
citing papers explorer
-
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing
MinerU2.5 uses a two-stage decoupled vision-language architecture to achieve state-of-the-art document parsing accuracy with lower computational overhead than existing general and domain-specific models.
-
Label-Efficient School Detection from Aerial Imagery via Weakly Supervised Pretraining and Fine-Tuning
A two-stage weakly supervised pipeline pretrains on auto-generated school labels from sparse points and fine-tunes on only 50 manual examples to achieve strong detection performance in aerial imagery.