mlsanityData to 14 Oct 2026

The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

arXiv 2103.06937 ↗First posted 11 Mar 2021cs.CV cs.LGcited by 21

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Abstract (arXiv)

This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~citesemi-aves at the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds sampled from the iNat-2018 dataset for a total of nearly 150k images. From this collection, we sample a subset of classes and their labels, while adding the images from the remaining classes to the unlabeled set of images. The presence of out-of-domain data (novel classes), high class-imbalance, and fine-grained similarity between classes poses significant challenges for existing semi-supervised recognition techniques in the literature. The dataset is available here: urlhttps://github.com/cvl-umass/semi-inat-2020

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 11 Mar 2021.

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15 of 24 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  3. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,085
  4. 2019MixMatch: A Holistic Approach to Semi-Supervised Learningcited by 1,391
  5. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,380
  6. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,311
  7. 2019Unsupervised Data Augmentation for Consistency Trainingcited by 1,060
  8. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,057
  9. 2017Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learningcited by 920
  10. 2020Rethinking Pre-training and Self-trainingcited by 352
  11. 2019ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoringcited by 349
  12. 2020Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learningcited by 14
3 more
  1. 2016Adapting Models to Signal Degradation using Distillationcited by 11
  2. 2020Semi-Supervised Recognition under a Noisy and Fine-grained Datasetcited by 2
  3. 2017Reasoning about Fine-grained Attribute Phrases using Reference Gamescited by 1

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Citing papers per year: 2021: 6; 2022: 2; 2023: 6; 2024: 3; 2025: 4.

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  1. 2022USB: A Unified Semi-supervised Learning Benchmark for Classificationcited by 95
  2. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 86
  3. 2021DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learningcited by 51
  4. 2021A Realistic Evaluation of Semi-Supervised Learning for Fine-Grained Classificationcited by 36
  5. 2021Revisiting Consistency Regularization for Semi-Supervised Learningcited by 34
  6. 2021The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshopcited by 16
  7. 2024Few-Shot Recognition via Stage-Wise Retrieval-Augmented Finetuningcited by 15
  8. 2023SemiReward: A General Reward Model for Semi-supervised Learningcited by 14
  9. 2023Prompting Scientific Names for Zero-Shot Species Recognitioncited by 14
  10. 2023Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurationscited by 9
  11. 2023Roll With the Punches: Expansion and Shrinkage of Soft Label Selection for Semi-supervised Fine-Grained Learningcited by 7
  12. 2022RDA: Reciprocal Distribution Alignment for Robust Semi-supervised Learningcited by 6
9 more
  1. 2021Fine-Grained Adversarial Semi-supervised Learningcited by 4
  2. 2023Exploration and Exploitation of Unlabeled Data for Open-Set Semi-Supervised Learningcited by 2
  3. 2025Active Learning via Vision-Language Model Adaptation with Open Datacited by 2
  4. 2024MaskMatch: Boosting Semi-Supervised Learning Through Mask Autoencoder-Driven Feature Learningcited by 1
  5. 2025Decoupled Entropy Minimizationcited by 1
  6. 2023SemiGPC: Distribution-Aware Label Refinement for Imbalanced Semi-Supervised Learning Using Gaussian Processescited by 0
  7. 2024Imbalanced Semi-Supervised Learning via Label Refinement and Threshold Adjustmentcited by 0
  8. 2025Solving Semi-Supervised Few-Shot Learning from an Auto-Annotation Perspectivecited by 0
  9. 2025Visual Species Recognition with Large Multimodal Models as Post-Hoc Correctorscited by 0