Attribute Prototype Network for Zero-Shot Learning

• URL: http://arxiv.org/abs/2008.08290v4
• Date: Thu, 6 May 2021 09:13:08 GMT
• Title: Attribute Prototype Network for Zero-Shot Learning
• Authors: Wenjia Xu, Yongqin Xian, Jiuniu Wang, Bernt Schiele, Zeynep Akata
• Abstract summary: We propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features. Our model points to the visual evidence of the attributes in an image, confirming the improved attribute localization ability of our image representation.
• Score: 113.50220968583353
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to unknown classes, we argue that an image representation with integrated attribute localization ability would be beneficial for zero-shot learning. To this end, we propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. We show that our locality augmented image representations achieve a new state-of-the-art on three zero-shot learning benchmarks. As an additional benefit, our model points to the visual evidence of the attributes in an image, e.g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.

Related papers

• High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning [54.86882315023791]
  We propose an innovative approach called High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning (HDAFL) HDAFL utilizes multiple convolutional kernels to automatically learn discriminative regions highly correlated with attributes in images. We also introduce a Transformer-based attribute discrimination encoder to enhance the discriminative capability among attributes.
  arXiv  Detail & Related papers  (2024-04-07T13:17:47Z)
• Attribute Localization and Revision Network for Zero-Shot Learning [13.530912616208722]
  Zero-shot learning enables the model to recognize unseen categories with the aid of auxiliary semantic information such as attributes. In this paper, we find that the choice between local and global features is not a zero-sum game, global features can also contribute to the understanding of attributes.
  arXiv  Detail & Related papers  (2023-10-11T14:50:52Z)
• Dual Feature Augmentation Network for Generalized Zero-shot Learning [14.410978100610489]
  Zero-shot learning (ZSL) aims to infer novel classes without training samples by transferring knowledge from seen classes. Existing embedding-based approaches for ZSL typically employ attention mechanisms to locate attributes on an image. We propose a novel Dual Feature Augmentation Network (DFAN), which comprises two feature augmentation modules.
  arXiv  Detail & Related papers  (2023-09-25T02:37:52Z)
• Attribute Prototype Network for Any-Shot Learning [113.50220968583353]
  We argue that an image representation with integrated attribute localization ability would be beneficial for any-shot, i.e. zero-shot and few-shot, image classification tasks. We propose a novel representation learning framework that jointly learns global and local features using only class-level attributes.
  arXiv  Detail & Related papers  (2022-04-04T02:25:40Z)
• TransZero++: Cross Attribute-Guided Transformer for Zero-Shot Learning [119.43299939907685]
  Zero-shot learning (ZSL) tackles the novel class recognition problem by transferring semantic knowledge from seen classes to unseen ones. Existing attention-based models have struggled to learn inferior region features in a single image by solely using unidirectional attention. We propose a cross attribute-guided Transformer network, termed TransZero++, to refine visual features and learn accurate attribute localization for semantic-augmented visual embedding representations.
  arXiv  Detail & Related papers  (2021-12-16T05:49:51Z)
• TransZero: Attribute-guided Transformer for Zero-Shot Learning [25.55614833575993]
  Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. We propose an attribute-guided Transformer network, TransZero, to refine visual features and learn attribute localization for discriminative visual embedding representations.
  arXiv  Detail & Related papers  (2021-12-03T02:39:59Z)
• Region Semantically Aligned Network for Zero-Shot Learning [18.18665627472823]
  We propose a Region Semantically Aligned Network (RSAN) which maps local features of unseen classes to their semantic attributes. We obtain each attribute from a specific region of the output and exploit these attributes for recognition. Experiments on several standard ZSL datasets reveal the benefit of the proposed RSAN method, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-14T03:23:40Z)
• Goal-Oriented Gaze Estimation for Zero-Shot Learning [62.52340838817908]
  We introduce a novel goal-oriented gaze estimation module (GEM) to improve the discriminative attribute localization. We aim to predict the actual human gaze location to get the visual attention regions for recognizing a novel object guided by attribute description. This work implies the promising benefits of collecting human gaze dataset and automatic gaze estimation algorithms on high-level computer vision tasks.
  arXiv  Detail & Related papers  (2021-03-05T02:14:57Z)
• CompGuessWhat?!: A Multi-task Evaluation Framework for Grounded Language Learning [78.3857991931479]
  We present GROLLA, an evaluation framework for Grounded Language Learning with Attributes. We also propose a new dataset CompGuessWhat?! as an instance of this framework for evaluating the quality of learned neural representations.
  arXiv  Detail & Related papers  (2020-06-03T11:21:42Z)

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.