Narrowing the Gap: Improved Detector Training with Noisy Location
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Narrowing the Gap: Improved Detector Training with Noisy Location Annotations

12 June 2022

Bing Li

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Papers citing "Narrowing the Gap: Improved Detector Training with Noisy Location Annotations"

6 / 6 papers shown

Title
ClipGrader: Leveraging Vision-Language Models for Robust Label Quality Assessment in Object Detection Hong Lu Yali Bian Rahul C. Shah ObjD VLM 131 0 0 03 Mar 2025
In Defense and Revival of Bayesian Filtering for Thermal Infrared Object Tracking Peng Gao Shi-Min Li Feng Gao Fei Wang Ruyue Yuan Hamido Fujita 88 9 0 27 Feb 2024
The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review Daniel Schwabe Katinka Becker Martin Seyferth Andreas Klass Tobias Schäffter 68 24 0 21 Feb 2024
Towards Building Self-Aware Object Detectors via Reliable Uncertainty Quantification and Calibration Kemal Oksuz Thomas Joy P. Dokania UQCV 82 16 0 03 Jul 2023
Embrace Limited and Imperfect Training Datasets: Opportunities and Challenges in Plant Disease Recognition Using Deep Learning Mingle Xu H. Kim Jucheng Yang A. Fuentes Yao Meng Sook Yoon Taehyun Kim D. Park 60 20 0 19 May 2023
Combating noisy labels in object detection datasets K. Chachula Jakub Lyskawa Bartlomiej Olber Piotr Fratczak A. Popowicz Krystian Radlak NoLa 48 4 0 25 Nov 2022