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Object-Centric Cropping for Visual Few-Shot Classification

arXiv 2508.00218 ↗First posted 31 Jul 2025cs.CV cs.LGcited by 0

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Abstract (arXiv)

In the domain of Few-Shot Image Classification, operating with as little as one example per class, the presence of image ambiguities stemming from multiple objects or complex backgrounds can significantly deteriorate performance. Our research demonstrates that incorporating additional information about the local positioning of an object within its image markedly enhances classification across established benchmarks. More importantly, we show that a significant fraction of the improvement can be achieved through the use of the Segment Anything Model, requiring only a pixel of the object of interest to be pointed out, or by employing fully unsupervised foreground object extraction methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 23 references that are in the map. First posted on arXiv 31 Jul 2025.

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References · 20

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2023Segment Anythingcited by 8,234
  5. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  6. 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
  7. 2018On First-Order Meta-Learning Algorithmscited by 1,018
  8. 2019A Closer Look at Few-shot Classificationcited by 684
  9. 2023Fast Segment Anythingcited by 290
  10. 2018Probabilistic Model-Agnostic Meta-Learningcited by 256
  11. 2021Localizing Objects with Self-Supervised Transformers and no Labelscited by 230
  12. 2023Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Modelscited by 91
8 more
  1. 2022Active Learning Helps Pretrained Models Learn the Intended Taskcited by 33
  2. 2023Transductive Few-shot Learning with Prototype-based Label Propagation by Iterative Graph Refinementcited by 30
  3. 2022Realistic Evaluation of Transductive Few-Shot Learningcited by 27
  4. 2024A Simple yet Effective Network based on Vision Transformer for Camouflaged Object and Salient Object Detectioncited by 23
  5. 2022MOVE: Unsupervised Movable Object Segmentation and Detectioncited by 20
  6. 2024Eliminating Feature Ambiguity for Few-Shot Segmentationcited by 19
  7. 2022Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classificationcited by 10
  8. 2016Application of Convolutional Neural Network for Image Classification on Pascal VOC Challenge 2012 datasetcited by 3

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