Long-tailed Recognition with Model Rebalancing

• URL: http://arxiv.org/abs/2510.08177v1
• Date: Thu, 09 Oct 2025 13:04:38 GMT
• Title: Long-tailed Recognition with Model Rebalancing
• Authors: Jiaan Luo, Feng Hong, Qiang Hu, Xiaofeng Cao, Feng Liu, Jiangchao Yao,
• Abstract summary: Long-tailed recognition is ubiquitous and challenging in deep learning.<n>We propose a novel framework, Model Rebalancing, which mitigates imbalance by directly rebalancing the model's parameter space.<n> MORE significantly improves generalization, particularly for tail classes, and effectively complements existing imbalance mitigation methods.
• Score: 34.149401963172934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-tailed recognition is ubiquitous and challenging in deep learning and even in the downstream finetuning of foundation models, since the skew class distribution generally prevents the model generalization to the tail classes. Despite the promise of previous methods from the perspectives of data augmentation, loss rebalancing and decoupled training etc., consistent improvement in the broad scenarios like multi-label long-tailed recognition is difficult. In this study, we dive into the essential model capacity impact under long-tailed context, and propose a novel framework, Model Rebalancing (MORE), which mitigates imbalance by directly rebalancing the model's parameter space. Specifically, MORE introduces a low-rank parameter component to mediate the parameter space allocation guided by a tailored loss and sinusoidal reweighting schedule, but without increasing the overall model complexity or inference costs. Extensive experiments on diverse long-tailed benchmarks, spanning multi-class and multi-label tasks, demonstrate that MORE significantly improves generalization, particularly for tail classes, and effectively complements existing imbalance mitigation methods. These results highlight MORE's potential as a robust plug-and-play module in long-tailed settings.

Related papers

• M-Loss: Quantifying Model Merging Compatibility with Limited Unlabeled Data [9.502531621979694]
  We introduce Merging-ensembling loss (M-Loss), a novel evaluation metric.<n>M-Loss quantifies the compatibility of merging source models using very limited unlabeled data.<n>Our theoretical analysis and empirical evaluations demonstrate that incorporating M-Loss into the merging process significantly improves the alignment between merged models and model ensembling.
  arXiv  Detail & Related papers  (2026-02-09T12:03:36Z)
• Long-Tailed Object Detection Pre-training: Dynamic Rebalancing Contrastive Learning with Dual Reconstruction [28.359463356384463]
  We introduce a novel pre-training framework for object detection, called Dynamic Rebalancing Contrastive Learning with Dual Reconstruction (2DRCL) Our method builds on a Holistic-Local Contrastive Learning mechanism, which aligns pre-training with object detection by capturing both global contextual semantics and detailed local patterns. Experiments on COCO and LVIS v1.0 datasets demonstrate the effectiveness of our method, particularly in improving the mAP/AP scores for tail classes.
  arXiv  Detail & Related papers  (2024-11-14T13:59:01Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• SMILE: Zero-Shot Sparse Mixture of Low-Rank Experts Construction From Pre-Trained Foundation Models [85.67096251281191]
  We present an innovative approach to model fusion called zero-shot Sparse MIxture of Low-rank Experts (SMILE) construction. SMILE allows for the upscaling of source models into an MoE model without extra data or further training. We conduct extensive experiments across diverse scenarios, such as image classification and text generation tasks, using full fine-tuning and LoRA fine-tuning.
  arXiv  Detail & Related papers  (2024-08-19T17:32:15Z)
• Enhanced Few-Shot Class-Incremental Learning via Ensemble Models [34.84881941101568]
  Few-shot class-incremental learning aims to continually fit new classes with limited training data. The main challenges are overfitting the rare new training samples and forgetting old classes. We propose a new ensemble model framework cooperating with data augmentation to boost generalization.
  arXiv  Detail & Related papers  (2024-01-14T06:07:07Z)
• Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning [52.021899899683675]
  In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited due to the under-representation of tail samples. We propose an Orthogonal Uncertainty Representation (OUR) of feature embedding and an end-to-end training strategy to improve the long-tail phenomenon of model robustness.
  arXiv  Detail & Related papers  (2023-10-16T05:50:34Z)
• Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual Manifolds [44.79535333220044]
  Recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets.<n>In this work, we first establish a geometric perspective for analyzing model fairness and then systematically propose a series of geometric measurements.
  arXiv  Detail & Related papers  (2023-03-22T04:49:23Z)
• Constructing Balance from Imbalance for Long-tailed Image Recognition [50.6210415377178]
  The imbalance between majority (head) classes and minority (tail) classes severely skews the data-driven deep neural networks. Previous methods tackle with data imbalance from the viewpoints of data distribution, feature space, and model design. We propose a concise paradigm by progressively adjusting label space and dividing the head classes and tail classes. Our proposed model also provides a feature evaluation method and paves the way for long-tailed feature learning.
  arXiv  Detail & Related papers  (2022-08-04T10:22:24Z)
• MixKD: Towards Efficient Distillation of Large-scale Language Models [129.73786264834894]
  We propose MixKD, a data-agnostic distillation framework, to endow the resulting model with stronger generalization ability. We prove from a theoretical perspective that under reasonable conditions MixKD gives rise to a smaller gap between the error and the empirical error. Experiments under a limited-data setting and ablation studies further demonstrate the advantages of the proposed approach.
  arXiv  Detail & Related papers  (2020-11-01T18:47:51Z)
• Long-tailed Recognition by Routing Diverse Distribution-Aware Experts [64.71102030006422]
  We propose a new long-tailed classifier called RoutIng Diverse Experts (RIDE) It reduces the model variance with multiple experts, reduces the model bias with a distribution-aware diversity loss, reduces the computational cost with a dynamic expert routing module. RIDE outperforms the state-of-the-art by 5% to 7% on CIFAR100-LT, ImageNet-LT and iNaturalist 2018 benchmarks.
  arXiv  Detail & Related papers  (2020-10-05T06:53:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.