# Leveraging Seen and Unseen Semantic Relationships for Generative Zero-Shot Learning

arXiv: https://arxiv.org/abs/2007.09549 · HTML: https://mlsanity.com/paper/2007.09549/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 30 of 64)

- [Graph Learning: A Survey](https://mlsanity.com/paper/2105.00696/) (2021)
- [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)
- [FREE: Feature Refinement for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2107.13807/) (2021)
- [HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning](https://mlsanity.com/paper/2109.15163/) (2021)
- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [MSDN: Mutually Semantic Distillation Network for Zero-Shot Learning](https://mlsanity.com/paper/2203.03137/) (2022)
- [OntoZSL: Ontology-enhanced Zero-shot Learning](https://mlsanity.com/paper/2102.07339/) (2021)
- [Zero-Shot Learning on 3D Point Cloud Objects and Beyond](https://mlsanity.com/paper/2104.04980/) (2021)
- [DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot Learning](https://mlsanity.com/paper/2207.01328/) (2022)
- [Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation](https://mlsanity.com/paper/2306.11087/) (2023)
- [Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2303.15322/) (2023)
- [Fine-Grained Zero-Shot Learning with DNA as Side Information](https://mlsanity.com/paper/2109.14133/) (2021)
- [Evolving Semantic Prototype Improves Generative Zero-Shot Learning](https://mlsanity.com/paper/2306.06931/) (2023)
- [Progressive Semantic-Guided Vision Transformer for Zero-Shot Learning](https://mlsanity.com/paper/2404.07713/) (2024)
- [Towards Novel Target Discovery Through Open-Set Domain Adaptation](https://mlsanity.com/paper/2105.02432/) (2021)
- [Hardness Sampling for Self-Training Based Transductive Zero-Shot Learning](https://mlsanity.com/paper/2106.00264/) (2021)
- [Disentangling Semantic-to-visual Confusion for Zero-shot Learning](https://mlsanity.com/paper/2106.08605/) (2021)
- [Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations](https://mlsanity.com/paper/2207.03824/) (2022)
- [The Curious Layperson: Fine-Grained Image Recognition without Expert Labels](https://mlsanity.com/paper/2111.03651/) (2021)
- [Semantic Feature Extraction for Generalized Zero-shot Learning](https://mlsanity.com/paper/2112.14478/) (2021)
- [A Survey on Visual Transfer Learning using Knowledge Graphs](https://mlsanity.com/paper/2201.11794/) (2022)
- [Unseen Classes at a Later Time? No Problem](https://mlsanity.com/paper/2203.16517/) (2022)
- [Vision Transformer-based Feature Extraction for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2302.00875/) (2023)
- [CIZSL++: Creativity Inspired Generative Zero-Shot Learning](https://mlsanity.com/paper/2101.00173/) (2021)
- [Region Semantically Aligned Network for Zero-Shot Learning](https://mlsanity.com/paper/2110.07130/) (2021)
- [Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning](https://mlsanity.com/paper/2311.14750/) (2023)
- [Structured Latent Embeddings for Recognizing Unseen Classes in Unseen Domains](https://mlsanity.com/paper/2107.05622/) (2021)
- [Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification](https://mlsanity.com/paper/2112.12927/) (2021)
- [Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without Real Data Exposure](https://mlsanity.com/paper/2403.09363/) (2024)

## Abstract (arXiv)

Zero-shot learning (ZSL) addresses the unseen class recognition problem by leveraging semantic information to transfer knowledge from seen classes to unseen classes. Generative models synthesize the unseen visual features and convert ZSL into a classical supervised learning problem. These generative models are trained using the seen classes and are expected to implicitly transfer the knowledge from seen to unseen classes. However, their performance is stymied by overfitting, which leads to substandard performance on Generalized Zero-Shot learning (GZSL). To address this concern, we propose the novel LsrGAN, a generative model that Leverages the Semantic Relationship between seen and unseen categories and explicitly performs knowledge transfer by incorporating a novel Semantic Regularized Loss (SR-Loss). The SR-loss guides the LsrGAN to generate visual features that mirror the semantic relationships between seen and unseen classes. Experiments on seven benchmark datasets, including the challenging Wikipedia text-based CUB and NABirds splits, and Attribute-based AWA, CUB, and SUN, demonstrates the superiority of the LsrGAN compared to previous state-of-the-art approaches under both ZSL and GZSL. Code is available at https: // github. com/ Maunil/ LsrGAN