SIM: an improved few-shot image classification model with multi-task learning-Reference-Cited by-同舟云学术

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SIM: an improved few-shot image classification model with multi-task learning

Published:2022-06-28 Issue:03 Volume:31 Page:
ISSN:1017-9909
Container-title:Journal of Electronic Imaging
language:
Short-container-title:J. Electron. Imag.

Author:

Guo Jin¹,Li Wengen¹,Guan Jihong¹,Gao Hang²,Liu Baobo²,Gong Lili³

Affiliation:

1. Tongji University, Department of Computer Science and Technology, Shanghai

2. Tongji University, Project Management Office of China National Scientific Seafloor Observatory, Shan

3. Shanghai Publishing and Printing College, Office of Academic Research, Shanghai

Funder

National Natural Science Foundation of China

Publisher

SPIE-Intl Soc Optical Eng

Subject

Electrical and Electronic Engineering,Computer Science Applications,Atomic and Molecular Physics, and Optics

Reference31 articles.

2. Multichannel Pulse-Coupled-Neural-Network-Based Color Image Segmentation for Object Detection

3. Human-level concept learning through probabilistic program induction

4. Few-Shot Learning for Domain-Specific Fine-Grained Image Classification

5. Model-agnostic meta-learning for fast adaptation of deep networks;Finn,2017

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2. TEG: image theme recognition using text-embedding-guided few-shot adaptation;Journal of Electronic Imaging;2024-01-24

3. Small samples data augmentation and improved MobileNet for surface defects classification of hot-rolled steel strips;Journal of Electronic Imaging;2022-12-24

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