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High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning

arXiv 2404.04953 ↗First posted 7 Apr 2024cs.CVcited by 0

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Abstract (arXiv)

Zero-shot learning(ZSL) aims to recognize new classes without prior exposure to their samples, relying on semantic knowledge from observed classes. However, current attention-based models may overlook the transferability of visual features and the distinctiveness of attribute localization when learning regional features in images. Additionally, they often overlook shared attributes among different objects. Highly discriminative attribute features are crucial for identifying and distinguishing unseen classes. To address these issues, we propose an innovative approach called High-Discriminative Attribute Feature Learning for Generalized Zero-Shot Learning (HDAFL). HDAFL optimizes visual features by learning attribute features to obtain discriminative visual embeddings. Specifically, HDAFL utilizes multiple convolutional kernels to automatically learn discriminative regions highly correlated with attributes in images, eliminating irrelevant interference in image features. Furthermore, we introduce a Transformer-based attribute discrimination encoder to enhance the discriminative capability among attributes. Simultaneously, the method employs contrastive loss to alleviate dataset biases and enhance the transferability of visual features, facilitating better semantic transfer between seen and unseen classes. Experimental results demonstrate the effectiveness of HDAFL across three widely used datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 38 references that are in the map. First posted on arXiv 7 Apr 2024.

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References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020Contrastive Learning with Hard Negative Samplescited by 619
  3. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 536
  4. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  5. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  6. 2015Label-Embedding for Image Classificationcited by 164
  7. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
  8. 2020Attribute Prototype Network for Zero-Shot Learningcited by 160
  9. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
  10. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
  11. 2021Contrastive Embedding for Generalized Zero-Shot Learningcited by 94
  12. 2021Goal-Oriented Gaze Estimation for Zero-Shot Learningcited by 68
12 more
  1. 2021FREE: Feature Refinement for Generalized Zero-Shot Learningcited by 67
  2. 2019Episode-based Prototype Generating Network for Zero-Shot Learningcited by 66
  3. 2019Transferable Contrastive Network for Generalized Zero-Shot Learningcited by 64
  4. 2020Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learningcited by 64
  5. 2021HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learningcited by 57
  6. 2020Generalized Zero-Shot Learning Via Over-Complete Distributioncited by 56
  7. 2020Invertible Zero-Shot Recognition Flowscited by 48
  8. 2018Discriminative Learning of Latent Features for Zero-Shot Recognitioncited by 24
  9. 2023Zero-Shot Learning by Harnessing Adversarial Samplescited by 24
  10. 2017Zero-Shot Learning via Class-Conditioned Deep Generative Modelscited by 21
  11. 2021Task Aligned Generative Meta-learning for Zero-shot Learningcited by 19
  12. 2021Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learningcited by 3

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