DISCO: Distribution-Aware Calibration for Object Detection with
DISCO: Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes Donghao Zhou1,2,3 Work was done during an internship at Tencent YouTu Lab. Jialin Li4 Jinpeng Li3
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Distribution-Aware Calibration for Object Detection with Noisy
Abstract Large-scale well-annotated datasets are of great importance for training an effective object detector. However, obtaining accurate bounding box annotations is laborious and demanding.
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Distribution-Aware Calibration for Object Detection with Noisy
Unfortunately, the resultant noisy bounding boxes could cause corrupt supervision signals and thus diminish detection performance. Motivated by the observation that the real ground-truth is usually
Distribution-Aware Calibration for Object Detection with Noisy
By modeling the spatial distribution of proposed bounding boxes and using this information to calibrate the supervision signals during training, DISCO can help object detection
Distribution-Aware Calibration for Object Detection with Noisy
DIStribution-aware CalibratiOn (DISCO) is proposed to model the spatial distribution of proposals for calibrating supervision signals and three distribution-aware techniques are developed
Distribution-Aware Calibration for Object Detection with Noisy
Motivated by the observation that the real ground-truth is usually situated in the aggregation region of the proposals assigned to a noisy ground-truth, we propose DIStribution-aware CalibratiOn (DISCO)
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ZHOU ET AL.: DISCO FOR OBJ. DET. WITH NOISY BOUNDING BOXES Distribution
lem termed object detec-tion with noisy bounding boxes. Motivated by the observation about proposal aggregation, we propose DISCO to calibrate the co rupt supervision signals. Spatial distribution
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DISCO: Distribution-Aware Calibration for Object Detection with Noisy
In this work, we propose DISCO to calibrate the corrupt su-pervision signals caused by noisy bounding boxes in object detection. Essentially, DISCO is a training-time calibration approach designed for two
DISCO_BMVC24_Poster
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes Donghao Zhou1, Jialin Li2, Jinpeng Li1, Jiancheng Huang3, Qiang Nie4,2, Yong Liu2†, Bin-Bin Gao2, Qiong Wang5, Pheng
[BMVC 2024] Distribution-Aware Calibration for Object Detection with
[BMVC 2024] Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes Abstract: Large-scale well-annotated datasets are of great importance for training an effective object detector.
DISCO_BMVC24_Poster
The proposed components of DISCO can all significantly help in detection performance. DISCO can offer superior improvement for classification, even approaching the results of training with clean