Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning-Reference-Cited by-同舟云学术

Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning

Published:2022-06 Issue: Volume: Page:
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Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

He Yangji¹,Liang Weihan¹,Zhao Dongyang¹,Zhou Hong-Yu²,Ge Weifeng¹,Yu Yizhou²,Zhang Wenqiang³

Affiliation:

1. School of Computer Science, Fudan University,Nebula AI Group

2. The University of Hong Kong

3. Shanghai Key Lab of Intelligent Information Processing

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879324.pdf?arnumber=9879324

Reference73 articles.

1. Binocular Mutual Learning for Improving Few-shot Classification

2. Looking Wider for Better Adaptive Representation in Few-Shot Learning

3. GraphFPN: Graph Feature Pyramid Network for Object Detection

4. Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition

5. TADAM: task dependent adaptive metric for improved few-shot learning;oreshkin;Advances in Neural Information Processing Systems 31 Annual Conference on Neural Information Processing Systems 2018 NeurIPS 2018,2018

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