SOTAVerified

Zero-Shot Image Classification

Zero-shot image classification is a technique in computer vision where a model can classify images into categories that were not present during training. This is achieved by leveraging semantic information about the categories, such as textual descriptions or relationships between classes.

Papers

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Showing 26–50 of 111 papers

TitleStatusHype
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationCode1
General Image Descriptors for Open World Image Retrieval using ViT CLIPCode1
Distilling Large Vision-Language Model with Out-of-Distribution GeneralizabilityCode1
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
Post-hoc Probabilistic Vision-Language ModelsCode1
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive LearningCode1
Babel-ImageNet: Massively Multilingual Evaluation of Vision-and-Language RepresentationsCode1
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image ClassificationCode1
LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer TextCode1
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
Masked Unsupervised Self-training for Label-free Image ClassificationCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
Generative Multi-Label Zero-Shot LearningCode1
Can We Talk Models Into Seeing the World Differently?Code1
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual KnowledgeCode1
LiT: Zero-Shot Transfer with Locked-image text TuningCode1
PerceptionCLIP: Visual Classification by Inferring and Conditioning on ContextsCode1
TaxaBind: A Unified Embedding Space for Ecological ApplicationsCode1
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions—0
Large-Scale Zero-Shot Image Classification from Rich and Diverse Textual Descriptions—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OpenClip H/14 (34B)(Laion2B)Top-1 accuracy30.01—Unverified
#ModelMetricClaimedVerifiedStatus
1CLIP (ViT B-32)Average Score56.64—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIP (Tiny A)Average Score11.4—Unverified