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 91–100 of 111 papers

TitleStatusHype
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—0
MADS: Multi-Attribute Document Supervision for Zero-Shot Image Classification—0
MoDE: CLIP Data Experts via Clustering—0
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
Noise-Tolerant Few-Shot Unsupervised Adapter for Vision-Language Models—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—0
PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining—0
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training—0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
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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