# Zero-Shot Learning via Joint Latent Similarity Embedding

arXiv: https://arxiv.org/abs/1511.04512 · HTML: https://mlsanity.com/paper/1511.04512/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2015.

## Built on by (most cited first, 30 of 39)

- [Decoupling Zero-Shot Semantic Segmentation](https://mlsanity.com/paper/2112.07910/) (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)
- [Zero-Shot Day-Night Domain Adaptation with a Physics Prior](https://mlsanity.com/paper/2108.05137/) (2021)
- [Zero-Shot Instance Segmentation](https://mlsanity.com/paper/2104.06601/) (2021)
- [Primitive Generation and Semantic-related Alignment for Universal Zero-Shot Segmentation](https://mlsanity.com/paper/2306.11087/) (2023)
- [Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2305.14335/) (2023)
- [Fold2Seq: A Joint Sequence(1D)-Fold(3D) Embedding-based Generative Model for Protein Design](https://mlsanity.com/paper/2106.13058/) (2021)
- [Embedding Transfer with Label Relaxation for Improved Metric Learning](https://mlsanity.com/paper/2103.14908/) (2021)
- [Towards Unbiased Multi-label Zero-Shot Learning with Pyramid and Semantic Attention](https://mlsanity.com/paper/2203.03483/) (2022)
- [Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning](https://mlsanity.com/paper/2103.15454/) (2021)
- [Learning Robust Visual-semantic Mapping for Zero-shot Learning](https://mlsanity.com/paper/2104.05668/) (2021)
- [MNER-QG: An End-to-End MRC framework for Multimodal Named Entity Recognition with Query Grounding](https://mlsanity.com/paper/2211.14739/) (2022)
- [Cross-modal Representation Learning for Zero-shot Action Recognition](https://mlsanity.com/paper/2205.01657/) (2022)
- [Disentangling Semantic-to-visual Confusion for Zero-shot Learning](https://mlsanity.com/paper/2106.08605/) (2021)
- [CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrieval](https://mlsanity.com/paper/2104.09918/) (2021)
- [GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning](https://mlsanity.com/paper/2309.00923/) (2023)
- [DPSeg: Dual-Prompt Cost Volume Learning for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2505.11676/) (2025)
- [Generative Replay-based Continual Zero-Shot Learning](https://mlsanity.com/paper/2101.08894/) (2021)
- [Zero-Shot Sketch Based Image Retrieval using Graph Transformer](https://mlsanity.com/paper/2201.10185/) (2022)
- [A Survey on Visual Transfer Learning using Knowledge Graphs](https://mlsanity.com/paper/2201.11794/) (2022)
- [Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains](https://mlsanity.com/paper/2108.08356/) (2021)
- [A Gating Model for Bias Calibration in Generalized Zero-shot Learning](https://mlsanity.com/paper/2203.04195/) (2022)
- [Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains](https://mlsanity.com/paper/2107.07497/) (2021)
- [Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baseline](https://mlsanity.com/paper/2302.07319/) (2023)
- [Rethinking Deep Contrastive Learning with Embedding Memory](https://mlsanity.com/paper/2103.14003/) (2021)
- [Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning](https://mlsanity.com/paper/2211.11751/) (2022)
- [RETVec: Resilient and Efficient Text Vectorizer](https://mlsanity.com/paper/2302.09207/) (2023)
- [Incrementally Zero-Shot Detection by an Extreme Value Analyzer](https://mlsanity.com/paper/2103.12609/) (2021)
- [Compositional Fine-Grained Low-Shot Learning](https://mlsanity.com/paper/2105.10438/) (2021)

## Abstract (arXiv)

Zero-shot recognition (ZSR) deals with the problem of predicting class labels for target domain instances based on source domain side information (e.g. attributes) of unseen classes. We formulate ZSR as a binary prediction problem. Our resulting classifier is class-independent. It takes an arbitrary pair of source and target domain instances as input and predicts whether or not they come from the same class, i.e. whether there is a match. We model the posterior probability of a match since it is a sufficient statistic and propose a latent probabilistic model in this context. We develop a joint discriminative learning framework based on dictionary learning to jointly learn the parameters of our model for both domains, which ultimately leads to our class-independent classifier. Many of the existing embedding methods can be viewed as special cases of our probabilistic model. On ZSR our method shows 4.90% improvement over the state-of-the-art in accuracy averaged across four benchmark datasets. We also adapt ZSR method for zero-shot retrieval and show 22.45% improvement accordingly in mean average precision (mAP).