Zero-Shot Learning

Zero-shot learning (ZSL) is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning.

Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning.

Benchmark datasets for zero-shot learning include aPY, AwA, and CUB, among others.

( Image credit: Prototypical Networks for Few shot Learning in PyTorch )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 501–525 of 1864 papers

Title	Date	Tasks	Status	Score
Learning Zero-Shot Material States Segmentation, by Implanting Natural Image Patterns in Synthetic Data	Mar 5, 2024	Material RecognitionSegmentation	CodeCode Available	5
Let's Transfer Transformations of Shared Semantic Representations	Mar 2, 2019	AttributeImage Retrieval	CodeCode Available	5
Customising General Large Language Models for Specialised Emotion Recognition Tasks	Apr 14, 2024	Emotion RecognitionLanguage Modeling	CodeCode Available	5
Learning Semantic Ambiguities for Zero-Shot Learning	Jan 5, 2022	Zero-Shot Learning	CodeCode Available	5
Language-Driven Anchors for Zero-Shot Adversarial Robustness	Jan 30, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available	5
Learning Invariant Visual Representations for Compositional Zero-Shot Learning	Jun 1, 2022	AttributeCompositional Zero-Shot Learning	CodeCode Available	5
Weakly Supervised Clustering by Exploiting Unique Class Count	Jun 18, 2019	ClusteringMultiple Instance Learning	CodeCode Available	5
Learning Portrait Style Representations	Dec 8, 2020	Image Generationzero-shot-classification	CodeCode Available	5
On the effectiveness of Large Language Models in the mechanical design domain	May 2, 2025	ClassificationSentence	CodeCode Available	5
On the Transferability of Visual Features in Generalized Zero-Shot Learning	Nov 22, 2022	Generalized Zero-Shot LearningKnowledge Distillation	CodeCode Available	5
CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity	May 16, 2025	Zero-Shot Learning	CodeCode Available	5
Anatomical grounding pre-training for medical phrase grounding	Feb 23, 2025	Phrase GroundingZero-Shot Learning	CodeCode Available	5
Learning Prototype via Placeholder for Zero-shot Recognition	Jul 29, 2022	Zero-Shot Learning	CodeCode Available	5
Learning to Promote Saliency Detectors	Jun 1, 2018	Saliency DetectionSmall Data Image Classification	CodeCode Available	5
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning	Apr 22, 2019	Zero-Shot Learning	CodeCode Available	5
Learning Deep Parsimonious Representations	Dec 1, 2016	ClusteringFew-Shot Image Classification	CodeCode Available	5
Automatic Report Generation for Histopathology images using pre-trained Vision Transformers	Nov 10, 2023	DecoderImage Segmentation	CodeCode Available	5
Learning Deep Representations of Fine-grained Visual Descriptions	May 17, 2016	AttributeImage Retrieval	CodeCode Available	5
Learning from Multiple Noisy Partial Labelers	Jun 8, 2021	AttributeClassification	CodeCode Available	5
Cross-Lingual Vision-Language Navigation	Oct 24, 2019	Domain AdaptationNavigate	CodeCode Available	5
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees	Dec 21, 2024	Graph Neural NetworkIn-Context Learning	CodeCode Available	5
Addressing target shift in zero-shot learning using grouped adversarial learning	Mar 2, 2020	AttributeZero-Shot Learning	CodeCode Available	5
Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization	Feb 1, 2023	Generalized Zero-Shot LearningMeta-Learning	CodeCode Available	5
Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning	Dec 5, 2024	Comment GenerationDecoder	CodeCode Available	5
Learning a Deep Embedding Model for Zero-Shot Learning	Nov 15, 2016	Image CaptioningSentence	CodeCode Available	5

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All datasets CUB-200-2011 MedConceptsQA SUN Attribute AwA2 Caltech-101 CIFAR-10 CIFAR-100 COCO-MLT DTD FGVC-Aircraft Flowers-102 Food-101

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ZeroDiff	average top-1 classification accuracy	87.5	—	Unverified
2	DUET	average top-1 classification accuracy	72.3	—	Unverified
3	Composer	average top-1 classification accuracy	69.4	—	Unverified
4	HDC-ZSC-MLP	average top-1 classification accuracy	65.6	—	Unverified
5	ZSL_TF-VAEGAN	average top-1 classification accuracy	64.9	—	Unverified
6	ZLaP	Accuracy	64.3	—	Unverified
7	ZLaP*	Accuracy	64.2	—	Unverified
8	HDC-ZSC	average top-1 classification accuracy	63.8	—	Unverified
9	SPOT	average top-1 classification accuracy	62.9	—	Unverified
10	f-VAEGAN-D2	average top-1 classification accuracy	61	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	dmis-lab/biobert-v1.1	Accuracy	26.15	—	Unverified
2	meta-llama/Meta-Llama-3-8B-Instruct	Accuracy	25.84	—	Unverified
3	epfl-llm/meditron-7b	Accuracy	25.75	—	Unverified
4	dmis-lab/meerkat-7b-v1.0	Accuracy	25.68	—	Unverified
5	meta-llama/Meta-Llama-3-8B-Instruct	Accuracy	25.65	—	Unverified
6	HuggingFaceH4/zephyr-7b-beta	Accuracy	25.54	—	Unverified
7	dmis-lab/biobert-v1.1	Accuracy	25.46	—	Unverified
8	epfl-llm/meditron-70b	Accuracy	25.36	—	Unverified
9	epfl-llm/meditron-70b	Accuracy	25.26	—	Unverified
10	HuggingFaceH4/zephyr-7b-beta	Accuracy	25.06	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZeroDiff	average top-1 classification accuracy	77.3	—	Unverified
2	SPOT (VAEGAN)	average top-1 classification accuracy	66.04	—	Unverified
3	ZSL_TF-VAEGAN	average top-1 classification accuracy	66	—	Unverified
4	f-VAEGAN	average top-1 classification accuracy	64.7	—	Unverified
5	DUET (Ours)	average top-1 classification accuracy	64.4	—	Unverified
6	LisGAN	average top-1 classification accuracy	61.7	—	Unverified
7	TCN	average top-1 classification accuracy	61.5	—	Unverified
8	f-CLSWGAN	average top-1 classification accuracy	60.8	—	Unverified
9	Cycle-WGAN	average top-1 classification accuracy	59.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZeroDiff	average top-1 classification accuracy	86.4	—	Unverified
2	ZSL-KG	average top-1 classification accuracy	78.08	—	Unverified
3	ZSL_TF-VAEGAN	average top-1 classification accuracy	72.2	—	Unverified
4	DUET (Ours)	average top-1 classification accuracy	69.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Accuracy	84	—	Unverified
2	ZLaP*	Accuracy	83.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP*	Accuracy	93.6	—	Unverified
2	ZLaP	Accuracy	93.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP*	Accuracy	74.2	—	Unverified
2	ZLaP	Accuracy	74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ViT-B/16	Average mAP	60.17	—	Unverified
2	ResNet-50	Average mAP	56.19	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Accuracy	51.2	—	Unverified
2	ZLaP*	Accuracy	51	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Accuracy	29.1	—	Unverified
2	ZLaP*	Accuracy	29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Accuracy	75.9	—	Unverified
2	ZLaP*	Accuracy	75.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP*	Accuracy	87.9	—	Unverified
2	ZLaP	Accuracy	87.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Top 1 Accuracy	72.1	—	Unverified
2	ZLaP*	Top 1 Accuracy	72.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HiTeA	Accuracy	21.7	—	Unverified
2	HiTeA	Accuracy	0.46	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	HiTeA	Accuracy	37.4	—	Unverified
2	HiTeA	Accuracy	0.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SPOT	average top-1 classification accuracy	71.9	—	Unverified
2	ZSL_TF-VAEGAN	average top-1 classification accuracy	70.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Accuracy	90	—	Unverified
2	ZLaP*	Accuracy	89	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP*	Accuracy	71.8	—	Unverified
2	ZLaP	Accuracy	71.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP*	Accuracy	71.4	—	Unverified
2	ZLaP	Accuracy	71	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP	Accuracy	76.3	—	Unverified
2	ZLaP*	Accuracy	76.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CLIP(ViT-B/16)	Average mAP	85.77	—	Unverified
2	CLIP(ResNet-50)	Average mAP	84.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZSL-KG	Top-1	60.54	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	zsl_ADA	Average Per-Class Accuracy	70.9	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZLaP*	Accuracy	63.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSDA	Pearson correlation coefficient (PCC)	0.52	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SeViLA	Accuracy	72.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	M^2-Encoder	Accuracy	80.7	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FrozenBiLM	Accuracy	51.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CZSL	A-acc	36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZS3Net	k=10 mIOU	26.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ZSL-KG	Accuracy	88.98	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VideoChat2	Accuracy	40.6	—	Unverified