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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 71–80 of 111 papers

TitleStatusHype
Class Knowledge Overlay to Visual Feature Learning for Zero-Shot Image Classification—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
CoAPT: Context Attribute words for Prompt Tuning—0
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Efficient Model-Agnostic Multi-Group Equivariant Networks—0
Efficient Multilingual Multi-modal Pre-training through Triple Contrastive Loss—0
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning—0
Gaze Embeddings for Zero-Shot Image Classification—0
Generative Negative Text Replay for Continual Vision-Language Pretraining—0
Show:102550
← PrevPage 8 of 12Next →
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