NMS-KSD: Efficient Knowledge Distillation for Dense Object Detection via Non-Maximum Suppression and Feature Storage

Recently, many studies have proposed knowledge distillation (KD) frameworks for object detection. However, these frameworks did not take into account the inefficiencies caused by the teacher detector. The inefficiency refers to the computational cost incurred during the process of passing input data...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Suho Son, Byung Cheol Song
Format:	Article
Language:	English
Published:	IEEE 2025-01-01
Subjects:	Dense object detection deep neural network compression knowledge distillation non-maximum suppression
Online Access:	https://ieeexplore.ieee.org/document/10988601/