Hierarchical confidence calibration and LoCLIP adaptation improve pseudo-label quality for open-vocabulary object detection, achieving new state-of-the-art results on COCO and LVIS benchmarks.
Probabilistic two-stage detection
3 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We develop a probabilistic interpretation of two-stage object detection. We show that this probabilistic interpretation motivates a number of common empirical training practices. It also suggests changes to two-stage detection pipelines. Specifically, the first stage should infer proper object-vs-background likelihoods, which should then inform the overall score of the detector. A standard region proposal network (RPN) cannot infer this likelihood sufficiently well, but many one-stage detectors can. We show how to build a probabilistic two-stage detector from any state-of-the-art one-stage detector. The resulting detectors are faster and more accurate than both their one- and two-stage precursors. Our detector achieves 56.4 mAP on COCO test-dev with single-scale testing, outperforming all published results. Using a lightweight backbone, our detector achieves 49.2 mAP on COCO at 33 fps on a Titan Xp, outperforming the popular YOLOv4 model.
years
2026 3representative citing papers
Hybrid T2I generation with teacher-student pseudo-labeling plus VRAIN context-aware I2I rare-class editing improves LVIS instance segmentation AP, especially on rare categories.
A two-stage nnUNet framework with patient-specific signed distance maps and wall-masked loss achieves 61.1% Dice and 1.711 mm ASSD for left atrial scar segmentation on the LAScarQS 2022 dataset.
citing papers explorer
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Exploring Hierarchical Consistency and Unbiased Objectness for Open-Vocabulary Object Detection
Hierarchical confidence calibration and LoCLIP adaptation improve pseudo-label quality for open-vocabulary object detection, achieving new state-of-the-art results on COCO and LVIS benchmarks.
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TMI: Text-to-Image Meets Image-to-Image for Complementary Data Synthesis to Boost Long-Tailed Instance Segmentation
Hybrid T2I generation with teacher-student pseudo-labeling plus VRAIN context-aware I2I rare-class editing improves LVIS instance segmentation AP, especially on rare categories.
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A Two Stage Pipeline for Left Atrial Wall Constrained Scar Segmentation and Localization from LGE-MR Images
A two-stage nnUNet framework with patient-specific signed distance maps and wall-masked loss achieves 61.1% Dice and 1.711 mm ASSD for left atrial scar segmentation on the LAScarQS 2022 dataset.