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 31–40 of 111 papers

TitleStatusHype
Mind's Eye: Image Recognition by EEG via Multimodal Similarity-Keeping Contrastive LearningCode1
Multimodal Adversarial Defense for Vision-Language Models by Leveraging One-To-Many Relationships—0
It's Not a Modality Gap: Characterizing and Addressing the Contrastive Gap—0
What Do You See? Enhancing Zero-Shot Image Classification with Multimodal Large Language ModelsCode0
Who's in and who's out? A case study of multimodal CLIP-filtering in DataCompCode0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
MoDE: CLIP Data Experts via ClusteringCode0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Learn "No" to Say "Yes" Better: Improving Vision-Language Models via NegationsCode1
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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