Variational Feature Disentangling for Fine-Grained Few-Shot Classification

• URL: http://arxiv.org/abs/2010.03255v3
• Date: Wed, 25 Aug 2021 02:46:11 GMT
• Title: Variational Feature Disentangling for Fine-Grained Few-Shot Classification
• Authors: Jingyi Xu and Hieu Le and Mingzhen Huang and ShahRukh Athar and Dimitris Samaras
• Abstract summary: Fine-grained few-shot recognition often suffers from the problem of training data scarcity for novel categories. In this paper, we focus one enlarging the intra-class variance of the unseen class to improve few-shot classification performance.
• Score: 30.350307891161865
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained few-shot recognition often suffers from the problem of training data scarcity for novel categories.The network tends to overfit and does not generalize well to unseen classes due to insufficient training data. Many methods have been proposed to synthesize additional data to support the training. In this paper, we focus one enlarging the intra-class variance of the unseen class to improve few-shot classification performance. We assume that the distribution of intra-class variance generalizes across the base class and the novel class. Thus, the intra-class variance of the base set can be transferred to the novel set for feature augmentation. Specifically, we first model the distribution of intra-class variance on the base set via variational inference. Then the learned distribution is transferred to the novel set to generate additional features, which are used together with the original ones to train a classifier. Experimental results show a significant boost over the state-of-the-art methods on the challenging fine-grained few-shot image classification benchmarks.

Related papers

• Covariance-based Space Regularization for Few-shot Class Incremental Learning [25.435192867105552]
  Few-shot Class Incremental Learning (FSCIL) requires the model to continually learn new classes with limited labeled data. Due to the limited data in incremental sessions, models are prone to overfitting new classes and suffering catastrophic forgetting of base classes. Recent advancements resort to prototype-based approaches to constrain the base class distribution and learn discriminative representations of new classes.
  arXiv  Detail & Related papers  (2024-11-02T08:03:04Z)
• Deep Similarity Learning Loss Functions in Data Transformation for Class Imbalance [2.693342141713236]
  In this paper, we use deep neural networks to train new representations of multi-class data. Our proposal modifies the distribution of features, i.e. the positions of examples in the learned embedded representation, and it does not modify the class sizes. Experiments with popular multi-class imbalanced benchmark data sets and three classifiers showed the advantage of the proposed approach.
  arXiv  Detail & Related papers  (2023-12-16T23:10:09Z)
• Multivariate Prototype Representation for Domain-Generalized Incremental Learning [35.83706574551515]
  We design a DGCIL approach that remembers old classes, adapts to new classes, and can classify reliably objects from unseen domains. Our loss formulation maintains classification boundaries and suppresses the domain-specific information of each class.
  arXiv  Detail & Related papers  (2023-09-24T06:42:04Z)
• Compound Batch Normalization for Long-tailed Image Classification [77.42829178064807]
  We propose a compound batch normalization method based on a Gaussian mixture. It can model the feature space more comprehensively and reduce the dominance of head classes. The proposed method outperforms existing methods on long-tailed image classification.
  arXiv  Detail & Related papers  (2022-12-02T07:31:39Z)
• Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning [99.14132861655223]
  We propose a novel intra-class adaptive augmentation (IAA) framework for deep metric learning. We reasonably estimate intra-class variations for every class and generate adaptive synthetic samples to support hard samples mining. Our method significantly improves and outperforms the state-of-the-art methods on retrieval performances by 3%-6%.
  arXiv  Detail & Related papers  (2022-11-29T14:52:38Z)
• Prediction Calibration for Generalized Few-shot Semantic Segmentation [101.69940565204816]
  Generalized Few-shot Semantic (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. We build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. Our PCN outperforms the state-the-art alternatives by large margins.
  arXiv  Detail & Related papers  (2022-10-15T13:30:12Z)
• Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport [78.9167477093745]
  We propose a novel distribution calibration method by learning the adaptive weight matrix between novel samples and base classes. Experimental results on standard benchmarks demonstrate that our proposed plug-and-play model outperforms competing approaches.
  arXiv  Detail & Related papers  (2022-10-09T02:32:57Z)
• Do Deep Networks Transfer Invariances Across Classes? [123.84237389985236]
  We show how a generative approach for learning the nuisance transformations can help transfer invariances across classes. Our results provide one explanation for why classifiers generalize poorly on unbalanced and longtailed distributions.
  arXiv  Detail & Related papers  (2022-03-18T04:38:18Z)
• Exploring Category-correlated Feature for Few-shot Image Classification [27.13708881431794]
  We present a simple yet effective feature rectification method by exploring the category correlation between novel and base classes as the prior knowledge. The proposed approach consistently obtains considerable performance gains on three widely used benchmarks.
  arXiv  Detail & Related papers  (2021-12-14T08:25:24Z)
• Ortho-Shot: Low Displacement Rank Regularization with Data Augmentation for Few-Shot Learning [23.465747123791772]
  In few-shot classification, the primary goal is to learn representations that generalize well for novel classes. We propose an efficient low displacement rank (LDR) regularization strategy termed Ortho-Shot.
  arXiv  Detail & Related papers  (2021-10-18T14:58:36Z)
• M2m: Imbalanced Classification via Major-to-minor Translation [79.09018382489506]
  In most real-world scenarios, labeled training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion. In this paper, we explore a novel yet simple way to alleviate this issue by augmenting less-frequent classes via translating samples from more-frequent classes. Our experimental results on a variety of class-imbalanced datasets show that the proposed method improves the generalization on minority classes significantly compared to other existing re-sampling or re-weighting methods.
  arXiv  Detail & Related papers  (2020-04-01T13:21:17Z)

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.