mlsanityData to 7 Oct 2026

LCReg: Long-Tailed Image Classification with Latent Categories based Recognition

arXiv 2309.07186 ↗First posted 13 Sep 2023cs.CVcited by 9

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Abstract (arXiv)

In this work, we tackle the challenging problem of long-tailed image recognition. Previous long-tailed recognition approaches mainly focus on data augmentation or re-balancing strategies for the tail classes to give them more attention during model training. However, these methods are limited by the small number of training images for the tail classes, which results in poor feature representations. To address this issue, we propose the Latent Categories based long-tail Recognition (LCReg) method. Our hypothesis is that common latent features shared by head and tail classes can be used to improve feature representation. Specifically, we learn a set of class-agnostic latent features shared by both head and tail classes, and then use semantic data augmentation on the latent features to implicitly increase the diversity of the training sample. We conduct extensive experiments on five long-tailed image recognition datasets, and the results show that our proposed method significantly improves the baselines.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 38 of its 52 references that are in the map. First posted on arXiv 13 Sep 2023.

Builds on

38 of 52 references.

References · 38

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26 more
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Built on by

Cited by year (2026 to 7 Oct)

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

9 papers.

  1. 2024Networking Systems for Video Anomaly Detection: A Tutorial and Surveycited by 21
  2. 2023Long-Tail Learning with Rebalanced Contrastive Losscited by 3
  3. 2024SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentationcited by 2
  4. 2024Reviving Undersampling for Long-Tailed Learningcited by 1
  5. 2024IMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Taskscited by 1
  6. 2026Decision Boundary-aware Generation for Long-tailed Learningcited by 1
  7. 2025PI-H2T: Enhancing Long-Tailed Visual Recognition with Permutation-Invariant and Head-to-Tail Feature Fusioncited by 0
  8. 2026Semantics-Aware Generative Latent Data Augmentation for Learning in Low-Resource Domainscited by 0
  9. 2026Generalised Balanced Softmax: A Finite-Data Perspective on Logit Adjustment for Long-Tailed Recognitioncited by 0