New ReDC framework improves object detector calibration with coordinate-level confidence

Researchers have developed a new post-hoc calibration framework called ReDC to improve the trustworthiness of deep learning-based object detectors. Unlike existing methods that focus on box-level localization, ReDC provides reliable coordinate-level confidence scores by considering directional information and coordinate-wise alignment. Experiments show that ReDC offers more precise localization accuracy than previous approaches and can also aggregate coordinate-level scores to represent box-level localization. AI

RANK_REASON The cluster contains a research paper detailing a new technical framework for object detection calibration. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• alphaXiv
• arXiv
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• ScienceCast

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →