mlsanityData to 14 Oct 2026

Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning

arXiv 2311.14750 ↗First posted 23 Nov 2023cs.CVcited by 6

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Abstract (arXiv)

Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing a series of unbiased visual-semantics mappings, wherein, the precision relies heavily on the completeness of extracted visual features from both seen and unseen classes. However, as a common practice in GZSL, the pre-trained feature extractor may easily exhibit difficulty in capturing domain-specific traits of the downstream tasks/datasets to provide fine-grained discriminative features, i.e., domain bias, which hinders the overall recognition performance, especially for unseen classes. Recent studies partially address this issue by fine-tuning feature extractors, while may inevitably incur catastrophic forgetting and overfitting issues. In this paper, we propose a simple yet effective Attribute-Aware Representation Rectification framework for GZSL, dubbed mathbf(AR)2, to adaptively rectify the feature extractor to learn novel features while keeping original valuable features. Specifically, our method consists of two key components, i.e., Unseen-Aware Distillation (UAD) and Attribute-Guided Learning (AGL). During training, UAD exploits the prior knowledge of attribute texts that are shared by both seen/unseen classes with attention mechanisms to detect and maintain unseen class-sensitive visual features in a targeted manner, and meanwhile, AGL aims to steer the model to focus on valuable features and suppress them to fit noisy elements in the seen classes by attribute-guided representation learning. Extensive experiments on various benchmark datasets demonstrate the effectiveness of our method.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 53 of its 79 references that are in the map. First posted on arXiv 23 Nov 2023.

Builds on

53 of 79 references.

References · 53

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38 more
  1. 2017Generalized Zero-Shot Learning via Synthesized Examplescited by 114
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Cited by year

Citing papers per year: 2024: 5; 2025: 1.

6 papers.

  1. 2024Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognitioncited by 19
  2. 2024Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognitioncited by 13
  3. 2024Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospectscited by 12
  4. 2024On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Surveycited by 5
  5. 2024Dual Expert Distillation Network for Generalized Zero-Shot Learningcited by 4
  6. 2025FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Datacited by 0