mlsanityData to 14 Oct 2026

On Implicit Attribute Localization for Generalized Zero-Shot Learning

arXiv 2103.04704 ↗First posted 8 Mar 2021cs.CVcited by 3

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Abstract (arXiv)

Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 32 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

18 of 32 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
  4. 2015Learning Deep Features for Discriminative Localizationcited by 2,436
  5. 2017Zero-Shot Learning -- A Comprehensive Evaluation of the Good, the Bad and the Uglycited by 538
  6. 2017Feature Generating Networks for Zero-Shot Learningcited by 257
  7. 2018Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoderscited by 206
  8. 2015Label-Embedding for Image Classificationcited by 165
  9. 2019f-VAEGAN-D2: A Feature Generating Framework for Any-Shot Learningcited by 164
  10. 2016Learning a Deep Embedding Model for Zero-Shot Learningcited by 153
  11. 2016An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wildcited by 146
  12. 2017A Generative Adversarial Approach for Zero-Shot Learning from Noisy Textscited by 96
6 more
  1. 2015Zero-Shot Learning via Semantic Similarity Embeddingcited by 74
  2. 2019Semantic-Guided Multi-Attention Localization for Zero-Shot Learningcited by 58
  3. 2020Invertible Zero-Shot Recognition Flowscited by 48
  4. 2018Preserving Semantic Relations for Zero-Shot Learningcited by 35
  5. 2019Creativity Inspired Zero-Shot Learningcited by 27
  6. 2020Bookworm continual learning: beyond zero-shot learning and continual learningcited by 3

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Cited by year

Citing papers per year: 2021: 1; 2022: 1; 2023: 1.

3 papers.

  1. 2022Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representationscited by 8
  2. 2021Implicit and Explicit Attention for Zero-Shot Learningcited by 5
  3. 2023Attribute Localization and Revision Network for Zero-Shot Learningcited by 0