A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation-Reference-Cited by-同舟云学术

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A New Few-Shot Learning-Based Model for Prohibited Objects Detection in Cluttered Baggage X-Ray Images Through Edge Detection and Reverse Validation

Published:2023 Issue: Volume:30 Page:1607-1611
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Liu Kaijun¹^ORCID,Lyu Shujing¹^ORCID,Shivakumara Palaiahnakote²^ORCID,Blumenstein Michael³^ORCID,Lu Yue¹^ORCID

Affiliation:

1. Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai, China

2. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia

3. University of Technology Sydney, Sydney, NSW, Australia

Funder

Science and Technology Commission of Shanghai Municipality

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/97/10036333/10288024.pdf?arnumber=10288024

Reference39 articles.

1. Matching networks for one shot learning;vinyals;Proc 30th Int Conf Neural Inf Process Syst,0

2. Sylph: A Hypernetwork Framework for Incremental Few-shot Object Detection

3. Pretraining representations for bioacoustic few-shot detection using supervised contrastive learning;moummad,2023

4. FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding

5. CASENet: Deep Category-Aware Semantic Edge Detection

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1. 3D effective atomic number estimation for x-ray security inspection systems;Anomaly Detection and Imaging with X-Rays (ADIX) IX;2024-06-07

2. X-YOLO: An Efficient Detection Network of Dangerous Objects in X-Ray Baggage Images;IEEE Signal Processing Letters;2024

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