mlsanityData to 7 Oct 2026

Semantic-Based Few-Shot Learning by Interactive Psychometric Testing

arXiv 2112.09201 ↗First posted 16 Dec 2021cs.CV cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

Few-shot classification tasks aim to classify images in query sets based on only a few labeled examples in support sets. Most studies usually assume that each image in a task has a single and unique class association. Under these assumptions, these algorithms may not be able to identify the proper class assignment when there is no exact matching between support and query classes. For example, given a few images of lions, bikes, and apples to classify a tiger. However, in a more general setting, we could consider the higher-level concept, the large carnivores, to match the tiger to the lion for semantic classification. Existing studies rarely considered this situation due to the incompatibility of label-based supervision with complex conception relationships. In this work, we advance the few-shot learning towards this more challenging scenario, the semantic-based few-shot learning, and propose a method to address the paradigm by capturing the inner semantic relationships using interactive psychometric learning. The experiment results on the CIFAR-100 dataset show the superiority of our method for the semantic-based few-shot learning compared to the baseline.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

25 of 39 references.

References · 25

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  3. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  6. 2017Prototypical Networks for Few-shot Learningcited by 3,001
  7. 2016Matching Networks for One Shot Learningcited by 2,246
  8. 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
  9. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
  10. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
  11. 2018Learning deep representations by mutual information estimation and maximizationcited by 1,265
  12. 2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
13 more
  1. 2016Colorful Image Colorizationcited by 1,113
  2. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 806
  3. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  4. 2019Data-Efficient Image Recognition with Contrastive Predictive Codingcited by 672
  5. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  6. 2016Learning Representations for Automatic Colorizationcited by 251
  7. 2016Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Predictioncited by 217
  8. 2017Representation Learning by Learning to Countcited by 120
  9. 2019Selfie: Self-supervised Pretraining for Image Embeddingcited by 46
  10. 2018Hierarchy-based Image Embeddings for Semantic Image Retrievalcited by 41
  11. 2019Learning Representations by Maximizing Mutual Information in Variational Autoencoderscited by 20
  12. 2020Memory-Augmented Relation Network for Few-Shot Learningcited by 10
  13. 2021Hierarchical Semantic Segmentation using Psychometric Learningcited by 2

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Aspect-Based Few-Shot Learningcited by 0