Extreme R-CNN: Few-Shot Object Detection via Sample Synthesis and Knowledge Distillation

Extreme R-CNN: Few-Shot Object Detection via Sample Synthesis and Knowledge Distillation

Traditional object detectors require extensive instance-level annotations for training. Conversely, few-shot object detectors, which are generally fine-tuned using limited data from unknown classes, tend to show biases toward base categories and are susceptible to variations within these unknown sam...

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Bibliographic Details
Main Authors: Shenyong Zhang, Wenmin Wang, Zhibing Wang, Honglei Li, Ruochen Li, Shixiong Zhang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Sensors
Subjects:
two-stage fine-tuning approach
few-shot object detection
synthesizing sample
knowledge distillation
triplet loss
Online Access:https://www.mdpi.com/1424-8220/24/23/7833
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