mlsanityData to 7 Oct 2026

Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection

arXiv 1804.01552 ↗First posted 4 Apr 2018cs.CVcited by 27

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Abstract (arXiv)

Self-supervision can dramatically cut back the amount of manually-labelled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection. We do so by building on several recent ideas in unsupervised landmark detection. Our approach learns dense distinctive visual descriptors from an unlabelled dataset of images using synthetic image transformations. It does so by means of a robust probabilistic formulation that can introspectively determine which image regions are likely to result in stable image matching. We show empirically that a network pre-trained in this manner requires significantly less supervision to learn semantic object parts compared to numerous pre-training alternatives. We also show that the pre-trained representation is excellent for semantic object matching.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Apr 2018.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 11; 2024: 4; 2025: 2.

27 papers.

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  1. 2021PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clusteringcited by 163
  2. 2022Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methodscited by 146
  3. 2021Learning Accurate Dense Correspondences and When to Trust Themcited by 113
  4. 2021PDC-Net+: Enhanced Probabilistic Dense Correspondence Networkcited by 80
  5. 2022Semi-Supervised and Unsupervised Deep Visual Learning: A Surveycited by 52
  6. 2023CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representationcited by 45
  7. 2023Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Trainingcited by 39
  8. 2021Few-shot Keypoint Detection with Uncertainty Learning for Unseen Speciescited by 27
  9. 2023ASIC: Aligning Sparse in-the-wild Image Collectionscited by 26
  10. 2023Unified Mask Embedding and Correspondence Learning for Self-Supervised Video Segmentationcited by 16
  11. 2021Learning Augmentation Distributions using Transformed Risk Minimizationcited by 13
  12. 2023From Saliency to DINO: Saliency-guided Vision Transformer for Few-shot Keypoint Detectioncited by 8
15 more
  1. 2022Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignmentcited by 7
  2. 2022Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalizationcited by 6
  3. 2023Learning Geometry-aware Representations by Sketchingcited by 5
  4. 2022Mix-up Self-Supervised Learning for Contrast-agnostic Applicationscited by 3
  5. 2023Representation Learning Dynamics of Self-Supervised Modelscited by 3
  6. 2024Self-Supervised Learning for Text Recognition: A Critical Surveycited by 3
  7. 2023ProcSim: Proxy-based Confidence for Robust Similarity Learningcited by 2
  8. 2025PartCo: Part-Level Correspondence Priors Enhance Category Discoverycited by 2
  9. 2023Curricular and Cyclical Loss for Time Series Learning Strategycited by 1
  10. 2024Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Datacited by 1
  11. 2024Spatial Context-based Self-Supervised Learning for Handwritten Text Recognitioncited by 1
  12. 2024Quantum Curriculum Learningcited by 1
  13. 2023Automatic Detection of Out-of-body Frames in Surgical Videos for Privacy Protection Using Self-supervised Learning and Minimal Labelscited by 0
  14. 2023Learning Spiking Neural Network from Easy to Hard taskcited by 0
  15. 2025Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detectioncited by 0