mlsanityData to 7 Oct 2026

Few-shot Object Localization

arXiv 2403.12466 ↗First posted 19 Mar 2024cs.CVcited by 2

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Abstract (arXiv)

Existing object localization methods are tailored to locate specific classes of objects, relying heavily on abundant labeled data for model optimization. However, acquiring large amounts of labeled data is challenging in many real-world scenarios, significantly limiting the broader application of localization models. To bridge this research gap, this paper defines a novel task named Few-Shot Object Localization (FSOL), which aims to achieve precise localization with limited samples. This task achieves generalized object localization by leveraging a small number of labeled support samples to query the positional information of objects within corresponding images. To advance this field, we design an innovative high-performance baseline model. This model integrates a dual-path feature augmentation module to enhance shape association and gradient differences between supports and query images, alongside a self query module to explore the association between feature maps and query images. Experimental results demonstrate a significant performance improvement of our approach in the FSOL task, establishing an efficient benchmark for further research. All codes and data are available at https://github.com/Ryh1218/FSOL.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 53 references that are in the map. First posted on arXiv 19 Mar 2024.

Builds on

32 of 53 references.

References · 32

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20 more
  1. 2021Learning To Count Everythingcited by 149
  2. 2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
  3. 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 123
  4. 2020Few-Shot Classification with Feature Map Reconstruction Networkscited by 116
  5. 2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
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  9. 2022An End-to-End Transformer Model for Crowd Localizationcited by 74
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  14. 2020Learning Independent Instance Maps for Crowd Localizationcited by 24
  15. 2022Localization Distillation for Object Detectioncited by 19
  16. 2017Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Trackingcited by 15
  17. 2022Crowd Localization from Gaussian Mixture Scoped Knowledge and Scoped Teachercited by 12
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  19. 2022Integrally Migrating Pre-trained Transformer Encoder-decoders for Visual Object Detectioncited by 9
  20. 2019Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsamplingcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024Text-guided Zero-Shot Object Localizationcited by 0
  2. 2026Personalized Object Identification and Localization via In-Context Inference with Vision-Language Modelscited by 0