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Zero-Shot Learning via Semantic Similarity Embedding

arXiv 1509.04767 ↗First posted 15 Sep 2015cs.CV stat.MLcited by 74

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Abstract (arXiv)

In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (eg attributes) for unseen classes. Our method is based on viewing each source or target data as a mixture of seen class proportions and we postulate that the mixture patterns have to be similar if the two instances belong to the same unseen class. This perspective leads us to learning source/target embedding functions that map an arbitrary source/target domain data into a same semantic space where similarity can be readily measured. We develop a max-margin framework to learn these similarity functions and jointly optimize parameters by means of cross validation. Our test results are compelling, leading to significant improvement in terms of accuracy on most benchmark datasets for zero-shot recognition.

Key facts. Cited by 74 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Sep 2015.

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Citing papers per year: 2021: 29; 2022: 20; 2023: 15; 2024: 4; 2025: 3; 2026: 3.

74 papers.

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  1. 2021Contrastive Embedding for Generalized Zero-Shot Learningcited by 94
  2. 2022Decoding Visual Neural Representations by Multimodal Learning of Brain-Visual-Linguistic Featurescited by 63
  3. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  4. 2021SIGN: Spatial-information Incorporated Generative Network for Generalized Zero-shot Semantic Segmentationcited by 41
  5. 2021Zero-Shot Instance Segmentationcited by 40
  6. 2022Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfercited by 33
  7. 2023A Systematic Review of Few-Shot Learning in Medical Imagingcited by 28
  8. 2023Zero-Shot Everything Sketch-Based Image Retrieval, and in Explainable Stylecited by 25
  9. 2023See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Datacited by 21
  10. 2021Fine-Grained Zero-Shot Learning with DNA as Side Informationcited by 20
  11. 2022Hybrid Routing Transformer for Zero-Shot Learningcited by 20
  12. 2021Learning Robust Visual-semantic Mapping for Zero-shot Learningcited by 16
38 more
  1. 2021Zero-shot Node Classification with Decomposed Graph Prototype Networkcited by 15
  2. 2023OpenForest: A data catalogue for machine learning in forest monitoringcited by 15
  3. 2021Multi-Head Self-Attention via Vision Transformer for Zero-Shot Learningcited by 12
  4. 2021Disentangling Semantic-to-visual Confusion for Zero-shot Learningcited by 11
  5. 2022Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attentioncited by 11
  6. 2021Conterfactual Generative Zero-Shot Semantic Segmentationcited by 10
  7. 2022Active Relation Discovery: Towards General and Label-aware Open Relation Extractioncited by 10
  8. 2021CrossATNet - A Novel Cross-Attention Based Framework for Sketch-Based Image Retrievalcited by 9
  9. 2025Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Modelscited by 9
  10. 2021Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learningcited by 8
  11. 2021Learning Visual Models using a Knowledge Graph as a Trainercited by 8
  12. 2021Boosting Generative Zero-Shot Learning by Synthesizing Diverse Features with Attribute Augmentationcited by 7
  13. 2022Zero-Shot Sketch Based Image Retrieval using Graph Transformercited by 7
  14. 2022A Survey on Visual Transfer Learning using Knowledge Graphscited by 7
  15. 2021CIZSL++: Creativity Inspired Generative Zero-Shot Learningcited by 6
  16. 2021Region Semantically Aligned Network for Zero-Shot Learningcited by 6
  17. 2021Domain Adaptation in Multi-View Embedding for Cross-Modal Video Retrievalcited by 6
  18. 2022How Robust are Discriminatively Trained Zero-Shot Learning Models?cited by 6
  19. 2022Deconstructed Generation-Based Zero-Shot Modelcited by 6
  20. 2021Implicit and Explicit Attention for Zero-Shot Learningcited by 5
  21. 2023Frustratingly Simple but Effective Zero-shot Detection and Segmentation: Analysis and a Strong Baselinecited by 5
  22. 2022Identifying Ambiguous Similarity Conditions via Semantic Matchingcited by 4
  23. 2023ParsNets: A Parsimonious Orthogonal and Low-Rank Linear Networks for Zero-Shot Learningcited by 4
  24. 2024A Survey on Large Language Models from Concept to Implementationcited by 4
  25. 2021On Implicit Attribute Localization for Generalized Zero-Shot Learningcited by 3
  26. 2021Online Lifelong Generalized Zero-Shot Learningcited by 3
  27. 2023Vocabulary-informed Zero-shot and Open-set Learningcited by 3
  28. 2023Adapt and Align to Improve Zero-Shot Sketch-Based Image Retrievalcited by 3
  29. 2023An Integral Projection-based Semantic Autoencoder for Zero-Shot Learningcited by 3
  30. 2021Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representationcited by 2
  31. 2022Resolving Semantic Confusions for Improved Zero-Shot Detectioncited by 2
  32. 2021Active learning for interactive satellite image change detectioncited by 1
  33. 2022Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detectorcited by 1
  34. 2022Reinforcement-based frugal learning for satellite image change detectioncited by 1
  35. 2022Learning Compositional Representations for Effective Low-Shot Generalizationcited by 1
  36. 2022Text2Model: Text-based Model Induction for Zero-shot Image Classificationcited by 1
  37. 2022DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learningcited by 1
  38. 2024Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunitiescited by 1

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