# Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier

arXiv: https://arxiv.org/abs/2007.09898 · HTML: https://mlsanity.com/paper/2007.09898/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 30 of 34)

- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [Targeted Supervised Contrastive Learning for Long-Tailed Recognition](https://mlsanity.com/paper/2111.13998/) (2021)
- [Learning of Visual Relations: The Devil is in the Tails](https://mlsanity.com/paper/2108.09668/) (2021)
- [Class-Incremental Learning with Strong Pre-trained Models](https://mlsanity.com/paper/2204.03634/) (2022)
- [A Simple Long-Tailed Recognition Baseline via Vision-Language Model](https://mlsanity.com/paper/2111.14745/) (2021)
- [Class-Balanced Distillation for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2104.05279/) (2021)
- [Constructing Balance from Imbalance for Long-tailed Image Recognition](https://mlsanity.com/paper/2208.02567/) (2022)
- [Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-tailed Learning](https://mlsanity.com/paper/2208.10043/) (2022)
- [Visually Consistent Hierarchical Image Classification](https://mlsanity.com/paper/2406.11608/) (2024)
- [Hierarchical classification at multiple operating points](https://mlsanity.com/paper/2210.10929/) (2022)
- [Long-Tailed Anomaly Detection with Learnable Class Names](https://mlsanity.com/paper/2403.20236/) (2024)
- [Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for Reducing Mistake Severity](https://mlsanity.com/paper/2303.05689/) (2023)
- [ProTeCt: Prompt Tuning for Taxonomic Open Set Classification](https://mlsanity.com/paper/2306.02240/) (2023)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail](https://mlsanity.com/paper/2304.00101/) (2023)
- [Hierarchical Selective Classification](https://mlsanity.com/paper/2405.11533/) (2024)
- [Video-based Hierarchical Species Classification for Longline Fishing Monitoring](https://mlsanity.com/paper/2102.03520/) (2021)
- [Confident AI](https://mlsanity.com/paper/2202.05957/) (2022)
- [Toward Unsupervised Realistic Visual Question Answering](https://mlsanity.com/paper/2303.05068/) (2023)
- [Alleviating the Effect of Data Imbalance on Adversarial Training](https://mlsanity.com/paper/2307.10205/) (2023)
- [Taxonomic Class Incremental Learning](https://mlsanity.com/paper/2304.05547/) (2023)
- [Free-Grained Hierarchical Visual Recognition](https://mlsanity.com/paper/2510.14737/) (2025)
- [IMB-NAS: Neural Architecture Search for Imbalanced Datasets](https://mlsanity.com/paper/2210.00136/) (2022)
- [Harnessing Superclasses for Learning from Hierarchical Databases](https://mlsanity.com/paper/2411.16438/) (2024)
- [Leveraging Data to Say No: Memory Augmented Plug-and-Play Selective Prediction](https://mlsanity.com/paper/2601.22570/) (2026)
- [Reframing Long-Tailed Learning via Loss Landscape Geometry](https://mlsanity.com/paper/2603.21217/) (2026)
- [Calibrating Class Activation Maps for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2108.12757/) (2021)
- [Bottom-up Hierarchical Classification Using Confusion-based Logit Compression](https://mlsanity.com/paper/2110.01756/) (2021)
- [Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports](https://mlsanity.com/paper/2403.17914/) (2024)

## Abstract (arXiv)

Long-tail recognition tackles the natural non-uniformly distributed data in real-world scenarios. While modern classifiers perform well on populated classes, its performance degrades significantly on tail classes. Humans, however, are less affected by this since, when confronted with uncertain examples, they simply opt to provide coarser predictions. Motivated by this, a deep realistic taxonomic classifier (Deep-RTC) is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions. The model has the option to reject classifying samples at different levels of the taxonomy, once it cannot guarantee the desired performance. Deep-RTC is implemented with a stochastic tree sampling during training to simulate all possible classification conditions at finer or coarser levels and a rejection mechanism at inference time. Experiments on the long-tailed version of four datasets, CIFAR100, AWA2, Imagenet, and iNaturalist, demonstrate that the proposed approach preserves more information on all classes with different popularity levels. Deep-RTC also outperforms the state-of-the-art methods in longtailed recognition, hierarchical classification, and learning with rejection literature using the proposed correctly predicted bits (CPB) metric.