# Preserving Semantic Relations for Zero-Shot Learning

arXiv: https://arxiv.org/abs/1803.03049 · HTML: https://mlsanity.com/paper/1803.03049/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 30 of 35)

- [Contrastive Embedding for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2103.16173/) (2021)
- [Goal-Oriented Gaze Estimation for Zero-Shot Learning](https://mlsanity.com/paper/2103.03433/) (2021)
- [Semantics-Guided Contrastive Network for Zero-Shot Object detection](https://mlsanity.com/paper/2109.06062/) (2021)
- [Dual Progressive Prototype Network for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2111.02073/) (2021)
- [Hybrid Routing Transformer for Zero-Shot Learning](https://mlsanity.com/paper/2203.15310/) (2022)
- [Learning Robust Visual-semantic Mapping for Zero-shot Learning](https://mlsanity.com/paper/2104.05668/) (2021)
- [Disentangling Semantic-to-visual Confusion for Zero-shot Learning](https://mlsanity.com/paper/2106.08605/) (2021)
- [Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention](https://mlsanity.com/paper/2204.04601/) (2022)
- [Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentation](https://mlsanity.com/paper/2112.12573/) (2021)
- [Cross Knowledge-based Generative Zero-Shot Learning Approach with Taxonomy Regularization](https://mlsanity.com/paper/2101.09892/) (2021)
- [Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs](https://mlsanity.com/paper/2107.06563/) (2021)
- [Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains](https://mlsanity.com/paper/2107.07497/) (2021)
- [Rethink, Revisit, Revise: A Spiral Reinforced Self-Revised Network for Zero-Shot Learning](https://mlsanity.com/paper/2112.00410/) (2021)
- [An Entropy-guided Reinforced Partial Convolutional Network for Zero-Shot Learning](https://mlsanity.com/paper/2111.02139/) (2021)
- [Dual Expert Distillation Network for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2404.16348/) (2024)
- [On Implicit Attribute Localization for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2103.04704/) (2021)
- [Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2104.01832/) (2021)
- [An Integral Projection-based Semantic Autoencoder for Zero-Shot Learning](https://mlsanity.com/paper/2306.14628/) (2023)
- [Pho(SC)-CTC -- A Hybrid Approach Towards Zero-shot Word Image Recognition](https://mlsanity.com/paper/2105.15093/) (2021)
- [Absolute Zero-Shot Learning](https://mlsanity.com/paper/2202.11319/) (2022)
- [LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning](https://mlsanity.com/paper/2207.12007/) (2022)
- [Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning](https://mlsanity.com/paper/2212.13151/) (2022)
- [Active learning for interactive satellite image change detection](https://mlsanity.com/paper/2110.04250/) (2021)
- [Reinforcement-based frugal learning for satellite image change detection](https://mlsanity.com/paper/2203.11564/) (2022)
- [Learning Compositional Representations for Effective Low-Shot Generalization](https://mlsanity.com/paper/2204.08090/) (2022)
- [Text2Model: Text-based Model Induction for Zero-shot Image Classification](https://mlsanity.com/paper/2210.15182/) (2022)
- [DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2103.10764/) (2021)
- [Zero-sample surface defect detection and classification based on semantic feedback neural network](https://mlsanity.com/paper/2106.07959/) (2021)
- [Using Fictitious Class Representations to Boost Discriminative Zero-Shot Learners](https://mlsanity.com/paper/2111.13550/) (2021)
- [Prototypical Model with Novel Information-theoretic Loss Function for Generalized Zero Shot Learning](https://mlsanity.com/paper/2112.03134/) (2021)

## Abstract (arXiv)

Zero-shot learning has gained popularity due to its potential to scale recognition models without requiring additional training data. This is usually achieved by associating categories with their semantic information like attributes. However, we believe that the potential offered by this paradigm is not yet fully exploited. In this work, we propose to utilize the structure of the space spanned by the attributes using a set of relations. We devise objective functions to preserve these relations in the embedding space, thereby inducing semanticity to the embedding space. Through extensive experimental evaluation on five benchmark datasets, we demonstrate that inducing semanticity to the embedding space is beneficial for zero-shot learning. The proposed approach outperforms the state-of-the-art on the standard zero-shot setting as well as the more realistic generalized zero-shot setting. We also demonstrate how the proposed approach can be useful for making approximate semantic inferences about an image belonging to a category for which attribute information is not available.