mlsanityData to 14 Oct 2026

Towards Fine-grained Visual Representations by Combining Contrastive Learning with Image Reconstruction and Attention-weighted Pooling

arXiv 2104.04323 ↗First posted 9 Apr 2021cs.CV cs.AI cs.LGcited by 13

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Abstract (arXiv)

This paper presents Contrastive Reconstruction, ConRec - a self-supervised learning algorithm that obtains image representations by jointly optimizing a contrastive and a self-reconstruction loss. We showcase that state-of-the-art contrastive learning methods (e.g. SimCLR) have shortcomings to capture fine-grained visual features in their representations. ConRec extends the SimCLR framework by adding (1) a self-reconstruction task and (2) an attention mechanism within the contrastive learning task. This is accomplished by applying a simple encoder-decoder architecture with two heads. We show that both extensions contribute towards an improved vector representation for images with fine-grained visual features. Combining those concepts, ConRec outperforms SimCLR and SimCLR with Attention-Pooling on fine-grained classification datasets.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 30 of its 39 references that are in the map. First posted on arXiv 9 Apr 2021.

Builds on

30 of 39 references.

References · 30

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,513
  5. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  6. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
  7. 2020Unsupervised Learning of Visual Features by Contrasting Cluster Assignmentscited by 2,857
  8. 2020Improved Baselines with Momentum Contrastive Learningcited by 2,287
  9. 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,591
  10. 2016Context Encoders: Feature Learning by Inpaintingcited by 1,428
  11. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  12. 2016Colorful Image Colorizationcited by 1,114
18 more
  1. 2015Unsupervised Visual Representation Learning by Context Predictioncited by 1,066
  2. 2019Self-Supervised Learning of Pretext-Invariant Representationscited by 808
  3. 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 437
  4. 2018Rethinking ImageNet Pre-trainingcited by 377
  5. 2018Audio-Visual Scene Analysis with Self-Supervised Multisensory Featurescited by 335
  6. 2019Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysiscited by 159
  7. 20203D Self-Supervised Methods for Medical Imagingcited by 134
  8. 2020Learning and Evaluating Representations for Deep One-class Classificationcited by 133
  9. 2019Cross-Domain Adaptation for Animal Pose Estimationcited by 108
  10. 2020The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classificationcited by 64
  11. 2019Multimodal Self-Supervised Learning for Medical Image Analysiscited by 57
  12. 2021CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretationcited by 57
  13. 2020Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histologycited by 43
  14. 2017Improving Landmark Localization with Semi-Supervised Learningcited by 39
  15. 2019Attribute-Aware Attention Model for Fine-grained Representation Learningcited by 22
  16. 2019Deep Learning for Fine-Grained Image Analysis: A Surveycited by 18
  17. 2019Self-supervised audio representation learning for mobile devicescited by 17
  18. 2020Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priorscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 3; 2023: 1; 2024: 4; 2025: 2; 2026: 2.

13 papers.

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  1. 2021How Transferable Are Self-supervised Features in Medical Image Classification Tasks?cited by 33
  2. 2024The Clever Hans Effect in Unsupervised Learningcited by 24
  3. 2023Label-Only Model Inversion Attacks via Knowledge Transfercited by 21
  4. 2024Model Inversion Robustness: Can Transfer Learning Help?cited by 12
  5. 2024On the Vulnerability of Skip Connections to Model Inversion Attackscited by 9
  6. 2022Keep Your Friends Close & Enemies Farther: Debiasing Contrastive Learning with Spatial Priors in 3D Radiology Imagescited by 5
  7. 2022Transfer Learning for Segmentation Problems: Choose the Right Encoder and Skip the Decodercited by 1
  8. 2022Robust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learningcited by 1
  9. 2024Exploiting Supervised Poison Vulnerability to Strengthen Self-Supervised Defensecited by 1
  10. 2025Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessmentcited by 1
  11. 2026Contrastive Learning Boosts Deterministic and Generative Models for Weather Datacited by 1
  12. 2025AdaViT: Adaptive Vision Transformer for Flexible Pretrain and Finetune with Variable 3D Medical Image Modalitiescited by 0
1 more
  1. 2026Template Collapse and Information-Theoretic Limits in Camera rPPG Pulse Morphology Restorationcited by 0