What question did this study set out to answer?

Provide a comprehensive overview of state-of-the-art few-shot object detection (FSOD) methodologies, benchmark datasets, and evaluation protocols.

April 17, 2023IEEE Transactions on Neural Networks and Learning SystemsOpen Access

Few-Shot Object Detection: A Comprehensive Survey

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Authors

MKMona KöhlerTechnische Universität Ilmenau MEMarkus EisenbachTechnische Universität Ilmenau HGHorst–Michael GroßTechnische Universität Ilmenau

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Implication

Systematic review categorizes training schemes and architectural layouts in few-shot object detection, highlighting evaluation challenges and guiding future machine learning frameworks.

Key Points

Provide a comprehensive overview of state-of-the-art few-shot object detection (FSOD) methodologies, benchmark datasets, and evaluation protocols.
Categorized existing FSOD approaches according to their underlying training schemes and architectural layouts.
Analyzed standard target domain datasets, benchmark evaluation protocols, and performance metrics across novel object categories.
Synthesized core architectural concepts and performance-enhancing strategies across leading FSOD methodologies.
Identified critical evaluation inconsistencies and benchmarking challenges across current FSOD experimental setups.
Outlined key emerging trends to improve novel category recognition using minimal annotated target training instances.

Cite This Study

Köhler et al. (2023) studied this question.

synapsesocial.com/papers/6a01bf7de8ec6bd19dcafb13 https://doi.org/10.1109/tnnls.2023.3265051

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Implication

Systematic review categorizes training schemes and architectural layouts in few-shot object detection, highlighting evaluation challenges and guiding future machine learning frameworks.

Few-Shot Object Detection: A Comprehensive Survey

Discussion

Member takes

Implication

Key Points

Cite This Study

Few-Shot Object Detection: A Comprehensive Survey

Discussion

Member takes

Implication

Key Points

Cite This Study

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