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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 61–70 of 111 papers

TitleStatusHype
Learning from Children: Improving Image-Caption Pretraining via CurriculumCode0
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
Text-to-Image Diffusion Models are Zero-Shot ClassifiersCode0
Structure Pretraining and Prompt Tuning for Knowledge Graph TransferCode1
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
Language-Driven Anchors for Zero-Shot Adversarial RobustnessCode0
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities—0
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse RetrievalCode1
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Show:102550
← PrevPage 7 of 12Next →
All datasetsCountry211ICinWODinW

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