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 51–60 of 111 papers

TitleStatusHype
KPL: Training-Free Medical Knowledge Mining of Vision-Language ModelsCode0
Retaining Knowledge and Enhancing Long-Text Representations in CLIP through Dual-Teacher Distillation—0
CLIP-PING: Boosting Lightweight Vision-Language Models with Proximus Intrinsic Neighbors Guidance—0
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives—0
Retrieval-enriched zero-shot image classification in low-resource domains—0
Multilingual Vision-Language Pre-training for the Remote Sensing DomainCode0
Altogether: Image Captioning via Re-aligning Alt-text—0
Open-vocabulary vs. Closed-set: Best Practice for Few-shot Object Detection Considering Text DescribabilityCode0
CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features—0
LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model—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