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 101–111 of 111 papers

TitleStatusHype
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Semantic Compositions Enhance Vision-Language Contrastive Learning—0
Soundify: Matching Sound Effects to Video—0
Text2Model: Text-based Model Induction for Zero-shot Image Classification—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Vision-Language Models Performing Zero-Shot Tasks Exhibit Gender-based Disparities—0
Visual-Semantic Embedding Model Informed by Structured Knowledge—0
When are Lemons Purple? The Concept Association Bias of Vision-Language Models—0
Zero-sample surface defect detection and classification based on semantic feedback neural network—0
Zero-Shot Image Classification Using Coupled Dictionary Embedding—0
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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