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Gestalt-Guided Image Understanding for Few-Shot Learning

arXiv 2302.03922 ↗First posted 8 Feb 2023cs.CVcited by 0

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Abstract (arXiv)

Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks. However, human can quickly learn the feature of a new category from very few samples. Nevertheless, previous work has rarely considered how to mimic human cognitive behavior and apply it to few-shot learning. This paper introduces Gestalt psychology to few-shot learning and proposes Gestalt-Guided Image Understanding, a plug-and-play method called GGIU. Referring to the principle of totality and the law of closure in Gestalt psychology, we design Totality-Guided Image Understanding and Closure-Guided Image Understanding to extract image features. After that, a feature estimation module is used to estimate the accurate features of images. Extensive experiments demonstrate that our method can improve the performance of existing models effectively and flexibly without retraining or fine-tuning. Our code is released on https://github.com/skingorz/GGIU.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 32 references that are in the map. First posted on arXiv 8 Feb 2023.

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17 of 32 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  5. 2017Prototypical Networks for Few-shot Learningcited by 3,015
  6. 2016Matching Networks for One Shot Learningcited by 2,255
  7. 2020Meta-Learning in Neural Networks: A Surveycited by 950
  8. 2018Dynamic Few-Shot Visual Learning without Forgettingcited by 385
  9. 2017Few-Shot Learning with Graph Neural Networkscited by 324
  10. 2020Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learningcited by 179
  11. 2021Free Lunch for Few-shot Learning: Distribution Calibrationcited by 165
  12. 2022NICO++: Towards Better Benchmarking for Domain Generalizationcited by 86
5 more
  1. 2020Boosting Few-Shot Learning With Adaptive Margin Losscited by 83
  2. 2019Associative Alignment for Few-shot Image Classificationcited by 58
  3. 2021Rectifying the Shortcut Learning of Background for Few-Shot Learningcited by 51
  4. 2021LibFewShot: A Comprehensive Library for Few-shot Learningcited by 21
  5. 2021Boosting Few-Shot Classification with View-Learnable Contrastive Learningcited by 11

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