mlsanityData to 7 Oct 2026

Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification

arXiv 2311.01212 ↗First posted 2 Nov 2023cs.CV cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of "Deep Cross-Domain Few-Shot Learning for Hyperspectral Image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 20 of its 64 references that are in the map. First posted on arXiv 2 Nov 2023.

Builds on

20 of 64 references.

References · 20

  1. 2017Attention Is All You Needcited by 59,932
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  3. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  4. 2020Supervised Contrastive Learningcited by 2,803
  5. 2016Matching Networks for One Shot Learningcited by 2,246
  6. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
  7. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  8. 2018Task-Agnostic Meta-Learning for Few-shot Learningcited by 151
  9. 2019Deep Learning for Hyperspectral Image Classification: An Overviewcited by 121
  10. 2021Deep Subdomain Adaptation Network for Image Classificationcited by 119
  11. 2019Edge-labeling Graph Neural Network for Few-shot Learningcited by 114
  12. 2020Graph Convolutional Networks for Hyperspectral Image Classificationcited by 113
8 more
  1. 2016Going Deeper with Contextual CNN for Hyperspectral Image Classificationcited by 58
  2. 2020Prototype Completion with Primitive Knowledge for Few-Shot Learningcited by 50
  3. 2019Transfer Learning with Dynamic Adversarial Adaptation Networkcited by 37
  4. 2021A Survey: Deep Learning for Hyperspectral Image Classification with Few Labeled Samplescited by 22
  5. 2020One-Shot Image Classification by Learning to Restore Prototypescited by 14
  6. 2021ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learningcited by 11
  7. 2019TGG: Transferable Graph Generation for Zero-shot and Few-shot Learningcited by 10
  8. 2017Siamese Neural Networks for One-shot detection of Railway Track Switchescited by 3

Built on by

Not cited yet.