Binocular Mutual Learning for Improving Few-shot Classification-Reference-Cited by-同舟云学术

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Binocular Mutual Learning for Improving Few-shot Classification

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Zhou Ziqi¹,Qiu Xi¹,Xie Jiangtao²,Wu Jianan¹,Zhang Chi¹

Affiliation:

1. Megvii Technology

2. Dalian University of Technology

Funder

Ministry of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711074.pdf?arnumber=9711074

Reference47 articles.

1. Matching networks for one shot learning;vinyals;arXiv preprint arXiv 1606 04080,2016

2. Attention is all you need;vaswani;arXiv preprint arXiv 1706 03762,2017

3. Prototypical networks for few-shot learning;snell;arXiv preprint arXiv 1703 05192,2017

4. ImageNet Large Scale Visual Recognition Challenge

5. Meta-learning for semi-supervised fewshot classification;ren;arXiv preprint arXiv 1803 00676,2018

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2. Cross-attention based dual-similarity network for few-shot learning;Pattern Recognition Letters;2024-10

3. Bimodal semantic fusion prototypical network for few-shot classification;Information Fusion;2024-09

4. Few-shot learning based on prototype rectification with a self-attention mechanism;Expert Systems with Applications;2024-09

5. Slippage Estimation via Few-Shot Learning Based on Wheel-Ruts Images for Wheeled Robots on Loose Soil;IEEE Transactions on Intelligent Transportation Systems;2024-07

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