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 81–90 of 111 papers

TitleStatusHype
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training—0
I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification—0
I2MVFormer: Large Language Model Generated Multi-View Document Supervision for Zero-Shot Image Classification—0
CLIPPO: Image-and-Language Understanding from Pixels Only—0
Improving Semantic Embedding Consistency by Metric Learning for Zero-Shot Classification—0
Integrating Propositional and Relational Label Side Information for Hierarchical Zero-Shot Image Classification—0
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap—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
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—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