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 11–20 of 111 papers

TitleStatusHype
PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent CollaborationCode2
Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality InversionCode2
What does a platypus look like? Generating customized prompts for zero-shot image classificationCode2
CHiLS: Zero-Shot Image Classification with Hierarchical Label SetsCode1
CamDiff: Camouflage Image Augmentation via Diffusion ModelCode1
DUET: Cross-modal Semantic Grounding for Contrastive Zero-shot LearningCode1
FILIP: Fine-grained Interactive Language-Image Pre-TrainingCode1
General Image Descriptors for Open World Image Retrieval using ViT CLIPCode1
Generative Multi-Label Zero-Shot LearningCode1
Disentangled Ontology Embedding for Zero-shot LearningCode1
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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