A Feature Generator for Few-Shot Learning

• URL: http://arxiv.org/abs/2409.14141v2
• Date: Fri, 11 Oct 2024 17:13:04 GMT
• Title: A Feature Generator for Few-Shot Learning
• Authors: Heethanjan Kanagalingam, Thenukan Pathmanathan, Navaneethan Ketheeswaran, Mokeeshan Vathanakumar, Mohamed Afham, Ranga Rodrigo,
• Abstract summary: Few-shot learning aims to enable models to recognize novel objects or classes with limited labelled data. We introduce a feature generator that creates visual features from class-level textual descriptions. Our results show a significant improvement in accuracy over baseline methods.
• Score: 2.4500728886415137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labelled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach outperforming the baseline model by 10% in 1-shot and around 5% in 5-shot approaches. Additionally, both visual-only and visual + textual generators have also been tested in this paper. The code is publicly available at https://github.com/heethanjan/Feature-Generator-for-FSL.

Related papers

• Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality Estimation [53.91958614666386]
  Unsupervised graph representation learning (UGRL) based on graph neural networks (GNNs) We propose a novel UGRL method based on Multi-hop feature Quality Estimation (MQE)
  arXiv  Detail & Related papers  (2024-07-29T12:24:28Z)
• Generative Multi-modal Models are Good Class-Incremental Learners [51.5648732517187]
  We propose a novel generative multi-modal model (GMM) framework for class-incremental learning. Our approach directly generates labels for images using an adapted generative model. Under the Few-shot CIL setting, we have improved by at least 14% accuracy over all the current state-of-the-art methods with significantly less forgetting.
  arXiv  Detail & Related papers  (2024-03-27T09:21:07Z)
• Exploiting Semantic Attributes for Transductive Zero-Shot Learning [97.61371730534258]
  Zero-shot learning aims to recognize unseen classes by generalizing the relation between visual features and semantic attributes learned from the seen classes. We present a novel transductive ZSL method that produces semantic attributes of the unseen data and imposes them on the generative process. Experiments on five standard benchmarks show that our method yields state-of-the-art results for zero-shot learning.
  arXiv  Detail & Related papers  (2023-03-17T09:09:48Z)
• Zero-Shot Logit Adjustment [89.68803484284408]
  Generalized Zero-Shot Learning (GZSL) is a semantic-descriptor-based learning technique. In this paper, we propose a new generation-based technique to enhance the generator's effect while neglecting the improvement of the classifier. Our experiments demonstrate that the proposed technique achieves state-of-the-art when combined with the basic generator, and it can improve various generative zero-shot learning frameworks.
  arXiv  Detail & Related papers  (2022-04-25T17:54:55Z)
• Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation [21.72622601533585]
  We propose a novel framework to boost Zero-Shot Learning (ZSL) by synthesizing diverse features. This method uses augmented semantic attributes to train the generative model, so as to simulate the real distribution of visual features. We evaluate the proposed model on four benchmark datasets, observing significant performance improvement against the state-of-the-art.
  arXiv  Detail & Related papers  (2021-12-23T14:32:51Z)
• FREE: Feature Refinement for Generalized Zero-Shot Learning [86.41074134041394]
  Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to overcoming the problems of visual-semantic domain gap and seen-unseen bias. Most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. We propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE) to tackle the above problem.
  arXiv  Detail & Related papers  (2021-07-29T08:11:01Z)
• Tensor feature hallucination for few-shot learning [17.381648488344222]
  Few-shot classification addresses the challenge of classifying examples given limited supervision and limited data. Previous works on synthetic data generation for few-shot classification focus on exploiting complex models. We investigate how a simple and straightforward synthetic data generation method can be used effectively.
  arXiv  Detail & Related papers  (2021-06-09T18:25:08Z)
• Discriminative feature generation for classification of imbalanced data [6.458496335718508]
  We propose a novel supervised discriminative feature generation (DFG) method for a minority class dataset. DFG is based on the modified structure of a generative adversarial network consisting of four independent networks. The experimental results show that the DFG generator enhances the augmentation of the label-preserved and diverse features.
  arXiv  Detail & Related papers  (2020-10-24T12:19:05Z)
• Learning Clusterable Visual Features for Zero-Shot Recognition [38.8104394191698]
  In zero-shot learning (ZSL), conditional generators have been widely used to generate additional training features. In this paper, we propose to learn clusterable features for ZSL problems. Experiments on SUN,CUB, and AWA2 datasets show consistent improvement over previous state-of-the-art ZSL results.
  arXiv  Detail & Related papers  (2020-10-07T07:58:55Z)
• Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks [55.11806035788036]
  It is advantageous to visualize the features considered to be essential for classification. Existing visualization methods develop high confidence images consisting of both background and foreground features. In this work, we propose a saliency-driven approach to visualize discriminative features that are considered most important for a given task.
  arXiv  Detail & Related papers  (2020-07-31T06:11:06Z)

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.