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 71–80 of 111 papers

TitleStatusHype
Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification—0
MoDE: CLIP Data Experts via Clustering—0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Image-Caption Encoding for Improving Zero-Shot GeneralizationCode0
Segment Any Change—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—0
LightCLIP: Learning Multi-Level Interaction for Lightweight Vision-Language Models—0
Towards Difficulty-Agnostic Efficient Transfer Learning for Vision-Language ModelsCode0
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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