mlsanityData to 7 Oct 2026

FAPIS: A Few-shot Anchor-free Part-based Instance Segmenter

arXiv 2104.00073 ↗First posted 31 Mar 2021cs.CVcited by 15

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Abstract (arXiv)

This paper is about few-shot instance segmentation, where training and test image sets do not share the same object classes. We specify and evaluate a new few-shot anchor-free part-based instance segmenter FAPIS. Our key novelty is in explicit modeling of latent object parts shared across training object classes, which is expected to facilitate our few-shot learning on new classes in testing. We specify a new anchor-free object detector aimed at scoring and regressing locations of foreground bounding boxes, as well as estimating relative importance of latent parts within each box. Also, we specify a new network for delineating and weighting latent parts for the final instance segmentation within every detected bounding box. Our evaluation on the benchmark COCO-20i dataset demonstrates that we significantly outperform the state of the art.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 31 Mar 2021.

Builds on

35 of 47 references.

References · 35

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
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  5. 2017Mask R-CNNcited by 7,259
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  8. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
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  10. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,160
  11. 2019FCOS: Fully Convolutional One-Stage Object Detectioncited by 1,573
  12. 2018Group Normalizationcited by 1,514
23 more
  1. 2019MMDetection: Open MMLab Detection Toolbox and Benchmarkcited by 1,379
  2. 2019Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regressioncited by 1,361
  3. 2019Objects as Pointscited by 1,172
  4. 2018Path Aggregation Network for Instance Segmentationcited by 796
  5. 2018CornerNet: Detecting Objects as Paired Keypointscited by 763
  6. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  7. 2019YOLACT: Real-time Instance Segmentationcited by 441
  8. 2017One-Shot Learning for Semantic Segmentationcited by 337
  9. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  10. 2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
  11. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  12. 2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
  13. 2018Instance-level Human Parsing via Part Grouping Networkcited by 134
  14. 2016Fully Convolutional Instance-aware Semantic Segmentationcited by 122
  15. 2017Recurrent Pixel Embedding for Instance Groupingcited by 57
  16. 2017MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Featurescited by 54
  17. 2016InstanceCut: from Edges to Instances with MultiCutcited by 48
  18. 2020FGN: Fully Guided Network for Few-Shot Instance Segmentationcited by 46
  19. 2018One-Shot Instance Segmentationcited by 43
  20. 2017Semantic Instance Segmentation via Deep Metric Learningcited by 37
  21. 2018Few-Shot Segmentation Propagation with Guided Networkscited by 29
  22. 2017Holistic, Instance-Level Human Parsingcited by 19
  23. 2019A New Local Transformation Module for Few-shot Segmentationcited by 15

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Citing papers per year: 2021: 3; 2022: 4; 2023: 4; 2024: 3; 2025: 1.

15 papers.

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  1. 2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
  2. 2021PartImageNet: A Large, High-Quality Dataset of Partscited by 107
  3. 2021Few-Shot Object Detection: A Comprehensive Surveycited by 28
  4. 2022iFS-RCNN: An Incremental Few-shot Instance Segmentercited by 19
  5. 2023MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentationcited by 18
  6. 2021Which images to label for few-shot medical landmark detection?cited by 16
  7. 2023Reference Twice: A Simple and Unified Baseline for Few-Shot Instance Segmentationcited by 12
  8. 2022VizWiz-FewShot: Locating Objects in Images Taken by People With Visual Impairmentscited by 11
  9. 2024BIV-Priv-Seg: Locating Private Content in Images Taken by People With Visual Impairmentscited by 5
  10. 2022Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmentercited by 3
  11. 2024Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentationcited by 3
  12. 2023ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentationcited by 2
3 more
  1. 2025Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentationcited by 1
  2. 2023SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Storecited by 0
  3. 2024Integrated Image-Text Based on Semi-supervised Learning for Small Sample Instance Segmentationcited by 0