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The Herbarium Challenge 2019 Dataset

arXiv 1906.05372 ↗First posted 12 Jun 2019cs.CV eess.IVcited by 59

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Abstract (arXiv)

Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them. In view of recent advances in computer vision and deep learning, developing an automated approach to help experts identify specimens could significantly accelerate research in this area. Whereas most existing botanical datasets comprise photos of specimens in the wild, herbarium sheets exhibit dried specimens, which poses new challenges. We present a challenge dataset of herbarium sheet images labeled by experts, with the intent of facilitating the development of automated identification techniques for this challenging scenario.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 7; 2024: 16; 2025: 17; 2026: 8.

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  1. 2022Generalized Category Discoverycited by 209
  2. 2022Parametric Classification for Generalized Category Discovery: A Baseline Studycited by 113
  3. 2023No Representation Rules Them All in Category Discoverycited by 61
  4. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  5. 2022Parametric Information Maximization for Generalized Category Discoverycited by 39
  6. 2023Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discoverycited by 39
  7. 2024Contrastive Mean-Shift Learning for Generalized Category Discoverycited by 39
  8. 2022OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learningcited by 37
  9. 2024SelEx: Self-Expertise in Fine-Grained Generalized Category Discoverycited by 34
  10. 2022OpenCon: Open-world Contrastive Learningcited by 33
  11. 2024Active Generalized Category Discoverycited by 31
  12. 2024Textual Knowledge Matters: Cross-Modality Co-Teaching for Generalized Visual Class Discoverycited by 30
38 more
  1. 2023CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discoverycited by 26
  2. 2024GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discoverycited by 26
  3. 2021Species Distribution Modeling for Machine Learning Practitioners: A Reviewcited by 21
  4. 2023Novel Class Discovery for Long-tailed Recognitioncited by 19
  5. 2025ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discoverycited by 19
  6. 2025DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discoverycited by 14
  7. 2025Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancementcited by 13
  8. 2025Dissecting Generalized Category Discovery: Multiplex Consensus under Self-Deconstructioncited by 13
  9. 2023Large-scale Pre-trained Models are Surprisingly Strong in Incremental Novel Class Discoverycited by 11
  10. 2024Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discoverycited by 9
  11. 2025SEAL: Semantic-Aware Hierarchical Learning for Generalized Category Discoverycited by 7
  12. 2024Contextuality Helps Representation Learning for Generalized Category Discoverycited by 6
  13. 2025Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularizationcited by 6
  14. 2022The Power of Transfer Learning in Agricultural Applications: AgriNetcited by 5
  15. 2024Interpret Your Decision: Logical Reasoning Regularization for Generalization in Visual Classificationcited by 5
  16. 2025Consistent Supervised-Unsupervised Alignment for Generalized Category Discoverycited by 5
  17. 2025Category Discovery: An Open-World Perspectivecited by 5
  18. 2023Generalized Category Discovery with Clustering Assignment Consistencycited by 4
  19. 2023Federated Continual Novel Class Learningcited by 4
  20. 2024Beyond Known Clusters: Probe New Prototypes for Efficient Generalized Class Discoverycited by 4
  21. 2024Labeled Data Selection for Category Discoverycited by 4
  22. 2024Zoom-shot: Fast and Efficient Unsupervised Zero-Shot Transfer of CLIP to Vision Encoders with Multimodal Losscited by 3
  23. 2025Generalized Category Discovery via Token Manifold Capacity Learningcited by 3
  24. 2025A Hidden Stumbling Block in Generalized Category Discovery: Distracted Attentioncited by 3
  25. 2024Revisiting Mutual Information Maximization for Generalized Category Discoverycited by 2
  26. 2024AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discoverycited by 2
  27. 2024A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorizationcited by 2
  28. 2025PartCo: Part-Level Correspondence Priors Enhance Category Discoverycited by 2
  29. 2025Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Expertscited by 2
  30. 2021Application of Computer Vision and Machine Learning for Digitized Herbarium Specimens: A Systematic Literature Reviewcited by 1
  31. 2021Fine-Grained Visual Classification of Plant Species In The Wild: Object Detection as A Reinforced Means of Attentioncited by 1
  32. 2024Exploiting Fine-Grained Prototype Distribution for Boosting Unsupervised Class Incremental Learningcited by 1
  33. 2024Composing Novel Classes: A Concept-Driven Approach to Generalized Category Discoverycited by 1
  34. 2025Generalized Category Discovery under the Long-Tailed Distributioncited by 1
  35. 2025Few-Shot Generalized Category Discovery With Retrieval-Guided Decision Boundary Enhancementcited by 1
  36. 2025FaceGCD: Generalized Face Discovery via Dynamic Prefix Generationcited by 1
  37. 2026Sparsity Hurts: Simple Linear Adapter Can Boost Generalized Category Discoverycited by 1
  38. 2021Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversitycited by 0

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