Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

• URL: http://arxiv.org/abs/2305.10648v2
• Date: Thu, 18 Jul 2024 06:33:49 GMT
• Title: Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition
• Authors: Mengke Li, Yiu-ming Cheung, Yang Lu, Zhikai Hu, Weichao Lan, Hui Huang,
• Abstract summary: We study the problem of long-tailed visual recognition from the perspective of feature level. Two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones.
• Score: 37.62659619941791
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This paper therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. Source code is available at https://github.com/Keke921/GCLLoss.

Related papers

• Dual Compensation Residual Networks for Class Imbalanced Learning [98.35401757647749]
  We propose Dual Compensation Residual Networks to better fit both tail and head classes. An important factor causing overfitting is that there is severe feature drift between training and test data on tail classes. We also propose a Residual Balanced Multi-Proxies classifier to alleviate the under-fitting issue.
  arXiv  Detail & Related papers  (2023-08-25T04:06:30Z)
• Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment [40.95064819012895]
  We observe that vanilla training on long-tailed data with cross-entropy loss makes the instance-rich head classes severely squeeze the spatial distribution of the tail classes. The original cross-entropy loss can only propagate gradient short-lively because the gradient in softmax form rapidly approaches zero as the logit difference increases. It is unfavorable for training on balanced data, but can be utilized to adjust the validity of the samples in long-tailed data.
  arXiv  Detail & Related papers  (2023-05-19T15:11:06Z)
• Inducing Neural Collapse in Deep Long-tailed Learning [13.242721780822848]
  We propose two explicit feature regularization terms to learn high-quality representation for class-imbalanced data. With the proposed regularization, Neural Collapse phenomena will appear under the class-imbalanced distribution. Our method is easily implemented, highly effective, and can be plugged into most existing methods.
  arXiv  Detail & Related papers  (2023-02-24T05:07:05Z)
• When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method [40.25499257944916]
  Real-world datasets are both noisily labeled and class-imbalanced. We propose a representation calibration method RCAL. We derive theoretical results to discuss the effectiveness of our representation calibration.
  arXiv  Detail & Related papers  (2022-11-20T11:36:48Z)
• 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)
• Learning Muti-expert Distribution Calibration for Long-tailed Video Classification [88.12433458277168]
  We propose an end-to-end multi-experts distribution calibration method based on two-level distribution information. By modeling this two-level distribution information, the model can consider the head classes and the tail classes. Our method achieves state-of-the-art performance on the long-tailed video classification task.
  arXiv  Detail & Related papers  (2022-05-22T09:52:34Z)
• Learning Debiased and Disentangled Representations for Semantic Segmentation [52.35766945827972]
  We propose a model-agnostic and training scheme for semantic segmentation. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2021-10-31T16:15:09Z)
• The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis [36.298869931803836]
  Long-tailed relation classification is a challenging problem as the head classes may dominate the training phase. We propose a robust classifier with attentive relation routing, which assigns soft weights by automatically aggregating the relations.
  arXiv  Detail & Related papers  (2020-09-15T12:47:00Z)
• Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier [68.38233199030908]
  Long-tail recognition tackles the natural non-uniformly distributed data in realworld scenarios. While moderns perform well on populated classes, its performance degrades significantly on tail classes. Deep-RTC is proposed as a new solution to the long-tail problem, combining realism with hierarchical predictions.
  arXiv  Detail & Related papers  (2020-07-20T05:57:42Z)

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.