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 61–70 of 111 papers

TitleStatusHype
MedUnifier: Unifying Vision-and-Language Pre-training on Medical Data with Vision Generation Task using Discrete Visual Representations—0
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision—0
Altogether: Image Captioning via Re-aligning Alt-text—0
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene—0
BaFTA: Backprop-Free Test-Time Adaptation For Zero-Shot Vision-Language Models—0
Bayesian Test-Time Adaptation for Vision-Language Models—0
Beyond the Visible: Multispectral Vision-Language Learning for Earth Observation—0
Bridge the Modality and Capability Gaps in Vision-Language Model Selection—0
CIBR: Cross-modal Information Bottleneck Regularization for Robust CLIP Generalization—0
CLAMP: Contrastive LAnguage Model Prompt-tuning—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